{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "\u003ca href=\"https://colab.research.google.com/github/tensorflow/tpu/blob/master/tools/colab/keras_mnist_tpu.ipynb\" target=\"_parent\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\u003c/a\u003e"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "N6ZDpd9XzFeN"
      },
      "source": [
        "##### Copyright 2018 The TensorFlow Hub Authors.\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "form",
        "colab": {},
        "colab_type": "code",
        "id": "KUu4vOt5zI9d"
      },
      "outputs": [],
      "source": [
        "# Copyright 2018 The TensorFlow Hub Authors. All Rights Reserved.\n",
        "#\n",
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "#     http://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License.\n",
        "# =============================================================================="
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "xqLjB2cy5S7m"
      },
      "source": [
        "## MNIST on TPU or GPU using tf.Keras and tf.data.Dataset\n",
        "\u003ctable\u003e\u003ctr\u003e\u003ctd\u003e\u003cimg valign=\"middle\" src=\"https://raw.githubusercontent.com/GoogleCloudPlatform/tensorflow-without-a-phd/master/tensorflow-rl-pong/images/keras-tensorflow-tpu300px.png\" width=\"300\" alt=\"Keras+Tensorflow+Cloud TPU\"\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/table\u003e\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "-d8ZNHaKhe5Y"
      },
      "source": [
        "## Overview\n",
        "\n",
        "This sample trains an \"MNIST\" handwritten digit \n",
        "recognition model on a GPU or TPU backend using a Keras\n",
        "model. Data are handled using the tf.data.Datset API. This is\n",
        "a very simple sample provided for educational purposes. Do\n",
        "not expect outstanding TPU performance on a dataset as\n",
        "small as MNIST.\n",
        "\n",
        "This notebook is hosted on GitHub. To view it in its original repository, after opening the notebook, select **File \u003e View on GitHub**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "dgAHfQtuhddd"
      },
      "source": [
        "## Learning objectives\n",
        "\n",
        "In this notebook, you will learn how to:\n",
        "*   Authenticate in Colab to access Google Cloud Storage (GSC)\n",
        "*   Format and prepare a dataset using tf.data.Dataset\n",
        "*   Create convolutional and dense layers using tf.keras.Sequential\n",
        "*   Build a Keras classifier with softmax, cross-entropy, and the adam optimizer\n",
        "*   Run training and validation in Keras using Cloud TPU\n",
        "*   Export a model for serving from ML Engine\n",
        "*   Deploy a trained model to ML Engine\n",
        "*  Test predictions on a deployed model\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "NM3bwCR39lW4"
      },
      "source": [
        "## Instructions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "_I0RdnOSkNmi"
      },
      "source": [
        "\u003ch3\u003e\u003ca href=\"https://cloud.google.com/gpu/\"\u003e\u003cimg valign=\"middle\" src=\"https://raw.githubusercontent.com/GoogleCloudPlatform/tensorflow-without-a-phd/master/tensorflow-rl-pong/images/gpu-hexagon.png\" width=\"50\"\u003e\u003c/a\u003e  \u0026nbsp;\u0026nbsp;Train on GPU or TPU\u0026nbsp;\u0026nbsp; \u003ca href=\"https://cloud.google.com/tpu/\"\u003e\u003cimg valign=\"middle\" src=\"https://raw.githubusercontent.com/GoogleCloudPlatform/tensorflow-without-a-phd/master/tensorflow-rl-pong/images/tpu-hexagon.png\" width=\"50\"\u003e\u003c/a\u003e\u003c/h3\u003e\n",
        "\n",
        "   1. On the main menu, click Runtime and select **Change runtime type**. Set \"TPU\" as the hardware accelerator.\n",
        "   1. Click Runtime again and select **Runtime \u003e Run All** .\n",
        " \n",
        "\u003ch3\u003e\u003ca href=\"https://cloud.google.com/ml-engine/\"\u003e\u003cimg valign=\"middle\" src=\"https://raw.githubusercontent.com/GoogleCloudPlatform/tensorflow-without-a-phd/master/tensorflow-rl-pong/images/mlengine-hexagon.png\" width=\"50\"\u003e\u003c/a\u003e  \u0026nbsp;\u0026nbsp;Deploy to Cloud Machine Learning (ML) Engine\u003c/h3\u003e\n",
        "* Near the end of this notebook you can deploy your trained model to ML Engine for a serverless, autoscaled, REST API experience. You will need a GCP project and a GCS bucket for this last part.\n",
        " 1. Create a Cloud Storage bucket at http://console.cloud.google.com/storage.\n",
        " 1. Fill the BUCKET parameter in the \"Configuration\" section below.\n",
        " 1. You can now run the cells under \"Deploy the trained model to ML Engine\"\n",
        "\n",
        "TPUs are located in Google Cloud, for optimal performance, they read data directly from Google Cloud Storage (GCS)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Lvo0t7XVIkWZ"
      },
      "source": [
        "## Data, model, and training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "cCpkS9C_H7Tl"
      },
      "outputs": [],
      "source": [
        "BATCH_SIZE = 128\n",
        "\n",
        "training_images_file   = 'gs://mnist-public/train-images-idx3-ubyte'\n",
        "training_labels_file   = 'gs://mnist-public/train-labels-idx1-ubyte'\n",
        "validation_images_file = 'gs://mnist-public/t10k-images-idx3-ubyte'\n",
        "validation_labels_file = 'gs://mnist-public/t10k-labels-idx1-ubyte'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "qpiJj8ym0v0-"
      },
      "source": [
        "### Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 1492,
          "status": "ok",
          "timestamp": 1550624358747,
          "user": {
            "displayName": "Garry Madrone",
            "photoUrl": "https://lh6.googleusercontent.com/-8C_CVnd65go/AAAAAAAAAAI/AAAAAAAAB4I/71ismvoohDg/s64/photo.jpg",
            "userId": "02335278249987270592"
          },
          "user_tz": 480
        },
        "id": "AoilhmYe1b5t",
        "outputId": "5f5a1118-089d-4902-c5a8-a5afbcfde54a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Tensorflow version 1.13.0-rc2\n"
          ]
        }
      ],
      "source": [
        "import os, re, math, json, shutil, pprint\n",
        "import PIL.Image, PIL.ImageFont, PIL.ImageDraw\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from matplotlib import pyplot as plt\n",
        "from tensorflow.python.platform import tf_logging\n",
        "print(\"Tensorflow version \" + tf.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "form",
        "colab": {},
        "colab_type": "code",
        "id": "qhdz68Xm3Z4Z"
      },
      "outputs": [],
      "source": [
        "#@title visualization utilities [RUN ME]\n",
        "\"\"\"\n",
        "This cell contains helper functions used for visualization\n",
        "and downloads only. You can skip reading it. There is very\n",
        "little useful Keras/Tensorflow code here.\n",
        "\"\"\"\n",
        "\n",
        "# Matplotlib config\n",
        "plt.rc('image', cmap='gray_r')\n",
        "plt.rc('grid', linewidth=0)\n",
        "plt.rc('xtick', top=False, bottom=False, labelsize='large')\n",
        "plt.rc('ytick', left=False, right=False, labelsize='large')\n",
        "plt.rc('axes', facecolor='F8F8F8', titlesize=\"large\", edgecolor='white')\n",
        "plt.rc('text', color='a8151a')\n",
        "plt.rc('figure', facecolor='F0F0F0')# Matplotlib fonts\n",
        "MATPLOTLIB_FONT_DIR = os.path.join(os.path.dirname(plt.__file__), \"mpl-data/fonts/ttf\")\n",
        "\n",
        "# pull a batch from the datasets. This code is not very nice, it gets much better in eager mode (TODO)\n",
        "def dataset_to_numpy_util(training_dataset, validation_dataset, N):\n",
        "  \n",
        "  # get one batch from each: 10000 validation digits, N training digits\n",
        "  unbatched_train_ds = training_dataset.apply(tf.data.experimental.unbatch())\n",
        "  v_images, v_labels = validation_dataset.make_one_shot_iterator().get_next()\n",
        "  t_images, t_labels = unbatched_train_ds.batch(N).make_one_shot_iterator().get_next()\n",
        "  \n",
        "  # Run once, get one batch. Session.run returns numpy results\n",
        "  with tf.Session() as ses:\n",
        "    (validation_digits, validation_labels,\n",
        "     training_digits, training_labels) = ses.run([v_images, v_labels, t_images, t_labels])\n",
        "  \n",
        "  # these were one-hot encoded in the dataset\n",
        "  validation_labels = np.argmax(validation_labels, axis=1)\n",
        "  training_labels = np.argmax(training_labels, axis=1)\n",
        "  \n",
        "  return (training_digits, training_labels,\n",
        "          validation_digits, validation_labels)\n",
        "\n",
        "# create digits from local fonts for testing\n",
        "def create_digits_from_local_fonts(n):\n",
        "  font_labels = []\n",
        "  img = PIL.Image.new('LA', (28*n, 28), color = (0,255)) # format 'LA': black in channel 0, alpha in channel 1\n",
        "  font1 = PIL.ImageFont.truetype(os.path.join(MATPLOTLIB_FONT_DIR, 'DejaVuSansMono-Oblique.ttf'), 25)\n",
        "  font2 = PIL.ImageFont.truetype(os.path.join(MATPLOTLIB_FONT_DIR, 'STIXGeneral.ttf'), 25)\n",
        "  d = PIL.ImageDraw.Draw(img)\n",
        "  for i in range(n):\n",
        "    font_labels.append(i%10)\n",
        "    d.text((7+i*28,0 if i\u003c10 else -4), str(i%10), fill=(255,255), font=font1 if i\u003c10 else font2)\n",
        "  font_digits = np.array(img.getdata(), np.float32)[:,0] / 255.0 # black in channel 0, alpha in channel 1 (discarded)\n",
        "  font_digits = np.reshape(np.stack(np.split(np.reshape(font_digits, [28, 28*n]), n, axis=1), axis=0), [n, 28*28])\n",
        "  return font_digits, font_labels\n",
        "\n",
        "# utility to display a row of digits with their predictions\n",
        "def display_digits(digits, predictions, labels, title, n):\n",
        "  plt.figure(figsize=(13,3))\n",
        "  digits = np.reshape(digits, [n, 28, 28])\n",
        "  digits = np.swapaxes(digits, 0, 1)\n",
        "  digits = np.reshape(digits, [28, 28*n])\n",
        "  plt.yticks([])\n",
        "  plt.xticks([28*x+14 for x in range(n)], predictions)\n",
        "  for i,t in enumerate(plt.gca().xaxis.get_ticklabels()):\n",
        "    if predictions[i] != labels[i]: t.set_color('red') # bad predictions in red\n",
        "  plt.imshow(digits)\n",
        "  plt.grid(None)\n",
        "  plt.title(title)\n",
        "  \n",
        "# utility to display multiple rows of digits, sorted by unrecognized/recognized status\n",
        "def display_top_unrecognized(digits, predictions, labels, n, lines):\n",
        "  idx = np.argsort(predictions==labels) # sort order: unrecognized first\n",
        "  for i in range(lines):\n",
        "    display_digits(digits[idx][i*n:(i+1)*n], predictions[idx][i*n:(i+1)*n], labels[idx][i*n:(i+1)*n],\n",
        "                   \"{} sample validation digits out of {} with bad predictions in red and sorted first\".format(n*lines, len(digits)) if i==0 else \"\", n)\n",
        "    \n",
        "# utility to display training and validation curves\n",
        "def display_training_curves(training, validation, title, subplot):\n",
        "  if subplot%10==1: # set up the subplots on the first call\n",
        "    plt.subplots(figsize=(10,10), facecolor='#F0F0F0')\n",
        "    plt.tight_layout()\n",
        "  ax = plt.subplot(subplot)\n",
        "  ax.grid(linewidth=1, color='white')\n",
        "  ax.plot(training)\n",
        "  ax.plot(validation)\n",
        "  ax.set_title('model '+ title)\n",
        "  ax.set_ylabel(title)\n",
        "  ax.set_xlabel('epoch')\n",
        "  ax.legend(['train', 'valid.'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Lzd6Qi464PsA"
      },
      "source": [
        "### Colab-only auth for this notebook and the TPU"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "both",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 59470,
          "status": "ok",
          "timestamp": 1550624437688,
          "user": {
            "displayName": "Garry Madrone",
            "photoUrl": "https://lh6.googleusercontent.com/-8C_CVnd65go/AAAAAAAAAAI/AAAAAAAAB4I/71ismvoohDg/s64/photo.jpg",
            "userId": "02335278249987270592"
          },
          "user_tz": 480
        },
        "id": "g3vQbAAFc_2W",
        "outputId": "8cac984e-788c-4d24-874a-02f36eb1bbe1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
            "For more information, please see:\n",
            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
            "  * https://github.com/tensorflow/addons\n",
            "If you depend on functionality not listed there, please file an issue.\n",
            "\n"
          ]
        }
      ],
      "source": [
        "IS_COLAB_BACKEND = 'COLAB_GPU' in os.environ  # this is always set on Colab, the value is 0 or 1 depending on GPU presence\n",
        "if IS_COLAB_BACKEND:\n",
        "  from google.colab import auth\n",
        "  auth.authenticate_user() # Authenticates the backend and also the TPU using your credentials so that they can access your private GCS buckets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Lz1Zknfk4qCx"
      },
      "source": [
        "### tf.data.Dataset: parse files and prepare training and validation datasets\n",
        "Please read the [best practices for building](https://www.tensorflow.org/guide/performance/datasets) input pipelines with tf.data.Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "ZE8dgyPC1_6m"
      },
      "outputs": [],
      "source": [
        "def read_label(tf_bytestring):\n",
        "    label = tf.decode_raw(tf_bytestring, tf.uint8)\n",
        "    label = tf.reshape(label, [])\n",
        "    label = tf.one_hot(label, 10)\n",
        "    return label\n",
        "  \n",
        "def read_image(tf_bytestring):\n",
        "    image = tf.decode_raw(tf_bytestring, tf.uint8)\n",
        "    image = tf.cast(image, tf.float32)/256.0\n",
        "    image = tf.reshape(image, [28*28])\n",
        "    return image\n",
        "  \n",
        "def load_dataset(image_file, label_file):\n",
        "    imagedataset = tf.data.FixedLengthRecordDataset(image_file, 28*28, header_bytes=16)\n",
        "    imagedataset = imagedataset.map(read_image, num_parallel_calls=16)\n",
        "    labelsdataset = tf.data.FixedLengthRecordDataset(label_file, 1, header_bytes=8)\n",
        "    labelsdataset = labelsdataset.map(read_label, num_parallel_calls=16)\n",
        "    dataset = tf.data.Dataset.zip((imagedataset, labelsdataset))\n",
        "    return dataset \n",
        "  \n",
        "def get_training_dataset(image_file, label_file, batch_size):\n",
        "    dataset = load_dataset(image_file, label_file)\n",
        "    dataset = dataset.cache()  # this small dataset can be entirely cached in RAM, for TPU this is important to get good performance from such a small dataset\n",
        "    dataset = dataset.shuffle(5000, reshuffle_each_iteration=True)\n",
        "    dataset = dataset.repeat() # Mandatory for Keras for now\n",
        "    dataset = dataset.batch(batch_size, drop_remainder=True) # drop_remainder is important on TPU, batch size must be fixed\n",
        "    dataset = dataset.prefetch(10)  # fetch next batches while training on the current one\n",
        "    return dataset\n",
        "  \n",
        "def get_validation_dataset(image_file, label_file):\n",
        "    dataset = load_dataset(image_file, label_file)\n",
        "    dataset = dataset.cache() # this small dataset can be entirely cached in RAM, for TPU this is important to get good performance from such a small dataset\n",
        "    dataset = dataset.batch(10000, drop_remainder=True) # 10000 items in eval dataset, all in one batch\n",
        "    dataset = dataset.repeat() # Mandatory for Keras for now\n",
        "    return dataset\n",
        "\n",
        "# instantiate the datasets\n",
        "training_dataset = get_training_dataset(training_images_file, training_labels_file, BATCH_SIZE)\n",
        "validation_dataset = get_validation_dataset(validation_images_file, validation_labels_file)\n",
        "\n",
        "# For TPU, we will need a function that returns the dataset\n",
        "training_input_fn = lambda: get_training_dataset(training_images_file, training_labels_file, BATCH_SIZE)\n",
        "validation_input_fn = lambda: get_validation_dataset(validation_images_file, validation_labels_file)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "_fXo6GuvL3EB"
      },
      "source": [
        "### Let's have a look at the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        },
        "colab_type": "code",
        "id": "yZ4tjPKvL2eh",
        "outputId": "ef6583a3-b1e6-4dd0-94db-8fe6499425b9"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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+/vx5waiqLVCIlZWVxthbVRmX+mNIzAWcRmKGSSwsLNS5a+fo0aMBAB07dmyQ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\nAq2MA9ymLSQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7f60cf3e2d30\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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e56lLly6SD7lv27ZNbHiCd1HTnDlzhjiOo4EDB0pabkZGBtna2lYISWnbti2t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\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7f60e484c278\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "N = 24\n",
        "(training_digits, training_labels,\n",
        " validation_digits, validation_labels) = dataset_to_numpy_util(training_dataset, validation_dataset, N)\n",
        "display_digits(training_digits, training_labels, training_labels, \"training digits and their labels\", N)\n",
        "display_digits(validation_digits[:N], validation_labels[:N], validation_labels[:N], \"validation digits and their labels\", N)\n",
        "font_digits, font_labels = create_digits_from_local_fonts(N)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "KIc0oqiD40HC"
      },
      "source": [
        "### Keras model: 3 convolutional layers, 2 dense layers\n",
        "If you are not sure what cross-entropy, dropout, softmax or batch-normalization mean, head here for a crash-course: [Tensorflow and deep learning without a PhD](https://github.com/GoogleCloudPlatform/tensorflow-without-a-phd/#featured-code-sample)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 680
        },
        "colab_type": "code",
        "id": "56y8UNFQIVwj",
        "outputId": "6c58ddd6-54f3-4902-e4d8-b3ce3dd1f7f5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "reshape (Reshape)            (None, 28, 28, 1)         0         \n",
            "_________________________________________________________________\n",
            "conv2d (Conv2D)              (None, 28, 28, 6)         54        \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo (None, 28, 28, 6)         18        \n",
            "_________________________________________________________________\n",
            "activation (Activation)      (None, 28, 28, 6)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 14, 14, 12)        2592      \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 14, 14, 12)        36        \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 14, 14, 12)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 7, 7, 24)          10368     \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 7, 7, 24)          72        \n",
            "_________________________________________________________________\n",
            "activation_2 (Activation)    (None, 7, 7, 24)          0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 1176)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 200)               235200    \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 200)               600       \n",
            "_________________________________________________________________\n",
            "activation_3 (Activation)    (None, 200)               0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 200)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                2010      \n",
            "=================================================================\n",
            "Total params: 250,950\n",
            "Trainable params: 250,466\n",
            "Non-trainable params: 484\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# This model trains to 99.4% sometimes 99.5% accuracy in 10 epochs (with a batch size of 32)\n",
        "\n",
        "l = tf.keras.layers\n",
        "model = tf.keras.Sequential(\n",
        "  [\n",
        "    l.Reshape(input_shape=(28*28,), target_shape=(28, 28, 1)),\n",
        "\n",
        "    l.Conv2D(filters=6, kernel_size=3, padding='same', use_bias=False), # no bias necessary before batch norm\n",
        "    l.BatchNormalization(scale=False, center=True), # no batch norm scaling necessary before \"relu\"\n",
        "    l.Activation('relu'), # activation after batch norm\n",
        "\n",
        "    l.Conv2D(filters=12, kernel_size=6, padding='same', use_bias=False, strides=2),\n",
        "    l.BatchNormalization(scale=False, center=True),\n",
        "    l.Activation('relu'),\n",
        "\n",
        "    l.Conv2D(filters=24, kernel_size=6, padding='same', use_bias=False, strides=2),\n",
        "    l.BatchNormalization(scale=False, center=True),\n",
        "    l.Activation('relu'),\n",
        "\n",
        "    l.Flatten(),\n",
        "    l.Dense(200, use_bias=False),\n",
        "    l.BatchNormalization(scale=False, center=True),\n",
        "    l.Activation('relu'),\n",
        "    l.Dropout(0.5), # Dropout on dense layer only\n",
        "\n",
        "    l.Dense(10, activation='softmax')\n",
        "  ])\n",
        "\n",
        "model.compile(optimizer='adam', # learning rate will be set by LearningRateScheduler\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "# print model layers\n",
        "model.summary()\n",
        "\n",
        "# set up learning rate decay\n",
        "lr_decay = tf.keras.callbacks.LearningRateScheduler(lambda epoch: 0.0001 + 0.02 * math.pow(0.5, 1+epoch), verbose=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "RxpRgF874-ix"
      },
      "source": [
        "### Train and validate the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1414
        },
        "colab_type": "code",
        "id": "TTwH_P-ZJ_xx",
        "outputId": "a568b1a3-d8e4-4f47-ae46-bf37b82c2995"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Querying Tensorflow master (b'grpc://10.80.162.170:8470') for TPU system metadata.\n",
            "INFO:tensorflow:Found TPU system:\n",
            "INFO:tensorflow:*** Num TPU Cores: 8\n",
            "INFO:tensorflow:*** Num TPU Workers: 1\n",
            "INFO:tensorflow:*** Num TPU Cores Per Worker: 8\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, -1, 5819649025652029442)\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 17179869184, 5738940317915696704)\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_GPU:0, XLA_GPU, 17179869184, 16797368249956209961)\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 17179869184, 13741170006794646354)\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 17179869184, 6941169112848048589)\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 17179869184, 535181099547651589)\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 17179869184, 1616341221270263986)\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 17179869184, 8793172071309422053)\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 17179869184, 10499848211451574385)\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 17179869184, 12703801782163726664)\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 17179869184, 18247638751695535613)\n",
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 17179869184, 206208722457089003)\n",
            "WARNING:tensorflow:tpu_model (from tensorflow.contrib.tpu.python.tpu.keras_support) is experimental and may change or be removed at any time, and without warning.\n",
            "INFO:tensorflow:Cloning Adam {'lr': 0.0010000000474974513, 'beta_1': 0.8999999761581421, 'beta_2': 0.9990000128746033, 'decay': 0.0, 'epsilon': 1e-07, 'amsgrad': False}\n",
            "INFO:tensorflow:Cloning Adam {'lr': 0.0010000000474974513, 'beta_1': 0.8999999761581421, 'beta_2': 0.9990000128746033, 'decay': 0.0, 'epsilon': 1e-07, 'amsgrad': False}\n",
            "\n",
            "Epoch 00001: LearningRateScheduler reducing learning rate to 0.0101.\n",
            "Epoch 1/10\n",
            "INFO:tensorflow:New input shapes; (re-)compiling: mode=train (# of cores 8), [TensorSpec(shape=(128,), dtype=tf.int32, name=None), TensorSpec(shape=(128, 784), dtype=tf.float32, name=None), TensorSpec(shape=(128, 10), dtype=tf.float32, name=None)]\n",
            "INFO:tensorflow:Overriding default placeholder.\n",
            "INFO:tensorflow:Cloning Adam {'lr': 0.010099999606609344, 'beta_1': 0.8999999761581421, 'beta_2': 0.9990000128746033, 'decay': 0.0, 'epsilon': 1e-07, 'amsgrad': False}\n",
            "INFO:tensorflow:Remapping placeholder for reshape_input\n",
            "INFO:tensorflow:KerasCrossShard: \u003ctensorflow.python.keras.optimizers.Adam object at 0x7f60c85fce48\u003e []\n",
            "INFO:tensorflow:Started compiling\n",
            "INFO:tensorflow:Finished compiling. Time elapsed: 3.294093132019043 secs\n",
            "INFO:tensorflow:Setting weights on TPU model.\n",
            "INFO:tensorflow:CPU -\u003e TPU lr: 0.010099999606609344 {0.0101}\n",
            "INFO:tensorflow:CPU -\u003e TPU beta_1: 0.8999999761581421 {0.9}\n",
            "INFO:tensorflow:CPU -\u003e TPU beta_2: 0.9990000128746033 {0.999}\n",
            "INFO:tensorflow:CPU -\u003e TPU decay: 0.0 {0.0}\n",
            "WARNING:tensorflow:Cannot update non-variable config: epsilon\n",
            "WARNING:tensorflow:Cannot update non-variable config: amsgrad\n",
            "462/468 [============================\u003e.] - ETA: 0s - loss: 0.0658 - acc: 0.9800INFO:tensorflow:New input shapes; (re-)compiling: mode=eval (# of cores 8), [TensorSpec(shape=(10000,), dtype=tf.int32, name=None), TensorSpec(shape=(10000, 784), dtype=tf.float32, name=None), TensorSpec(shape=(10000, 10), dtype=tf.float32, name=None)]\n",
            "INFO:tensorflow:Overriding default placeholder.\n",
            "INFO:tensorflow:Cloning Adam {'lr': 0.010099999606609344, 'beta_1': 0.8999999761581421, 'beta_2': 0.9990000128746033, 'decay': 0.0, 'epsilon': 1e-07, 'amsgrad': False}\n",
            "INFO:tensorflow:Remapping placeholder for reshape_input\n",
            "INFO:tensorflow:KerasCrossShard: \u003ctensorflow.python.keras.optimizers.Adam object at 0x7f60c621d908\u003e []\n",
            "INFO:tensorflow:Started compiling\n",
            "INFO:tensorflow:Finished compiling. Time elapsed: 2.260793924331665 secs\n",
            "468/468 [==============================] - 21s 45ms/step - loss: 0.0652 - acc: 0.9802 - val_loss: 0.0460 - val_acc: 0.9865\n",
            "\n",
            "Epoch 00002: LearningRateScheduler reducing learning rate to 0.0051.\n",
            "Epoch 2/10\n",
            "468/468 [==============================] - 4s 9ms/step - loss: 0.0096 - acc: 0.9971 - val_loss: 0.0499 - val_acc: 0.9859\n",
            "\n",
            "Epoch 00003: LearningRateScheduler reducing learning rate to 0.0026.\n",
            "Epoch 3/10\n",
            "468/468 [==============================] - 4s 9ms/step - loss: 0.0036 - acc: 0.9991 - val_loss: 0.0290 - val_acc: 0.9915\n",
            "\n",
            "Epoch 00004: LearningRateScheduler reducing learning rate to 0.00135.\n",
            "Epoch 4/10\n",
            "468/468 [==============================] - 4s 9ms/step - loss: 0.0021 - acc: 0.9996 - val_loss: 0.0274 - val_acc: 0.9926\n",
            "\n",
            "Epoch 00005: LearningRateScheduler reducing learning rate to 0.0007250000000000001.\n",
            "Epoch 5/10\n",
            "468/468 [==============================] - 5s 10ms/step - loss: 0.0014 - acc: 0.9998 - val_loss: 0.0299 - val_acc: 0.9919\n",
            "\n",
            "Epoch 00006: LearningRateScheduler reducing learning rate to 0.0004125.\n",
            "Epoch 6/10\n",
            "468/468 [==============================] - 5s 10ms/step - loss: 0.0011 - acc: 0.9998 - val_loss: 0.0293 - val_acc: 0.9920\n",
            "\n",
            "Epoch 00007: LearningRateScheduler reducing learning rate to 0.00025625.\n",
            "Epoch 7/10\n",
            "468/468 [==============================] - 4s 9ms/step - loss: 9.1853e-04 - acc: 0.9998 - val_loss: 0.0294 - val_acc: 0.9920\n",
            "\n",
            "Epoch 00008: LearningRateScheduler reducing learning rate to 0.000178125.\n",
            "Epoch 8/10\n",
            "468/468 [==============================] - 4s 9ms/step - loss: 7.9986e-04 - acc: 0.9999 - val_loss: 0.0296 - val_acc: 0.9920\n",
            "\n",
            "Epoch 00009: LearningRateScheduler reducing learning rate to 0.0001390625.\n",
            "Epoch 9/10\n",
            "468/468 [==============================] - 4s 9ms/step - loss: 7.7266e-04 - acc: 0.9999 - val_loss: 0.0303 - val_acc: 0.9921\n",
            "\n",
            "Epoch 00010: LearningRateScheduler reducing learning rate to 0.00011953125000000001.\n",
            "Epoch 10/10\n",
            "468/468 [==============================] - 4s 9ms/step - loss: 6.8227e-04 - acc: 0.9999 - val_loss: 0.0301 - val_acc: 0.9922\n"
          ]
        }
      ],
      "source": [
        "EPOCHS = 10\n",
        "steps_per_epoch = 60000//BATCH_SIZE  # 60,000 items in this dataset\n",
        "tpu = None\n",
        "trained_model = model\n",
        "\n",
        "# Counting steps and batches on TPU: the tpu.keras_to_tpu_model API regards the batch size of the input dataset\n",
        "# as the per-core batch size. The effective batch size is 8x more because Cloud TPUs have 8 cores. It increments\n",
        "# the step by +8 everytime a global batch (8 per-core batches) is processed. Therefore batch size and steps_per_epoch\n",
        "# settings can stay as they are for TPU training. The training will just go faster.\n",
        "# Warning: this might change in the final version of the Keras/TPU API.\n",
        "\n",
        "try: # TPU detection\n",
        "  tpu = tf.contrib.cluster_resolver.TPUClusterResolver() # Picks up a connected TPU on Google's Colab, ML Engine, Kubernetes and Deep Learning VMs accessed through the 'ctpu up' utility\n",
        "  #tpu = tf.contrib.cluster_resolver.TPUClusterResolver('MY_TPU_NAME') # If auto-detection does not work, you can pass the name of the TPU explicitly (tip: on a VM created with \"ctpu up\" the TPU has the same name as the VM)\n",
        "except ValueError:\n",
        "  print('Training on GPU/CPU')\n",
        "  \n",
        "if tpu: # TPU training\n",
        "  strategy = tf.contrib.tpu.TPUDistributionStrategy(tpu)\n",
        "  trained_model = tf.contrib.tpu.keras_to_tpu_model(model, strategy=strategy)\n",
        "  # Work in progress: reading directly from dataset object not yet implemented\n",
        "  # for Keras/TPU. Keras/TPU needs a function that returns a dataset.\n",
        "  history = trained_model.fit(training_input_fn, steps_per_epoch=steps_per_epoch, epochs=EPOCHS,\n",
        "                            validation_data=validation_input_fn, validation_steps=1, callbacks=[lr_decay])\n",
        "else: # GPU/CPU training\n",
        "  history = trained_model.fit(training_dataset, steps_per_epoch=steps_per_epoch, epochs=EPOCHS,\n",
        "                              validation_data=validation_dataset, validation_steps=1, callbacks=[lr_decay])  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Q961axfSIMRB"
      },
      "source": [
        "### Visualize training and validation curves"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 767
        },
        "colab_type": "code",
        "id": "ji4ZVkwaKQMD",
        "outputId": "3b23af80-c88b-480d-a16e-573f735334c2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
          ]
        },
        {
          "data": {
            "image/png": 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sZ6BuHDPUasPg1d0ruxnqtGEw1D92rePWP46BCrsTm83W5AH8TPwewKOioigqKvJaV1pa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\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7f60d532da20\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "print(history.history.keys())\n",
        "display_training_curves(history.history['acc'], history.history['val_acc'], 'accuracy', 211)\n",
        "display_training_curves(history.history['loss'], history.history['val_loss'], 'loss', 212)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "9jFVovcUUVs1"
      },
      "source": [
        "### Visualize predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 778
        },
        "colab_type": "code",
        "id": "w12OId8Mz7dF",
        "outputId": "e0346f58-c67f-4086-ac23-1acde06abe72"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:New input shapes; (re-)compiling: mode=infer (# of cores 8), [TensorSpec(shape=(3, 784), dtype=tf.float32, name='reshape_input_10')]\n",
            "INFO:tensorflow:Overriding default placeholder.\n",
            "INFO:tensorflow:Cloning Adam {'lr': 0.010099999606609344, 'beta_1': 0.8999999761581421, 'beta_2': 0.9990000128746033, 'decay': 0.0, 'epsilon': 1e-07, 'amsgrad': False}\n",
            "INFO:tensorflow:Remapping placeholder for reshape_input\n",
            "INFO:tensorflow:Started compiling\n",
            "INFO:tensorflow:Finished compiling. Time elapsed: 1.3434703350067139 secs\n",
            "INFO:tensorflow:New input shapes; (re-)compiling: mode=infer (# of cores 8), [TensorSpec(shape=(1250, 784), dtype=tf.float32, name='reshape_input_10')]\n",
            "INFO:tensorflow:Overriding default placeholder.\n",
            "INFO:tensorflow:Remapping placeholder for reshape_input\n",
            "INFO:tensorflow:Started compiling\n",
            "INFO:tensorflow:Finished compiling. Time elapsed: 1.3039665222167969 secs\n"
          ]
        },
        {
          "data": {
            "image/png": 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atWpQ5RvK119/TV9//TUBoMjISEG1akMul3Mtqk1JeXk5ASBvb2/BNBYsWEAA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\nGAwGg8FgMIwEC8YZDAaDwWAwGAwj8X9BZm3VBvZuywAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7f60c5f854a8\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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5+vRpYhhGo8q4VCqlvn37quUb0CzKeFlZGS1atIjmzZtH4eHhFB4e\nThKJpNH51kRkZCQBoGXLltGyZctIJpNpTFZNCASCJrGKl5SU0NWrV8nGxoYmTpxImZmZVFRUpHaL\naW3yhUIhSaVSsrCwoDFjxqg1f4lEQuPGjaPRo0fTkydPamw3EomEVdifP3+u1jIokEqlJJVK6ebN\nm+Ti4kIMw9DChQvV2r4EAgEZGBjQunXraN26dSSXy4mIKCAggKZMmUIxMTHsudjYWNLT01O7Yrhq\n1SraunUrBQQEUEBAADvTcPv2bUpJSaGzZ8+Si4sLubi4KCnnCxYsUFtdPHjwgCwtLcnAwIAKCgqa\nrD3HxMRQTEwMJSUlsefKy8vJ3d2d/eiMHj1a7QPtiIgI9mO9Z88eCgoK0qhiXFkZV/W3JpFKpRQf\nH08bNmwgW1tbMjY2pkGDBtGgQYNo586dFBsbq7J+X716Va8yKpTxqVOn0vnz58nOzo4cHR3p6dOn\n9PTpU0pMTKTMzEzKzMykdevW0aBBg9jfAAB5eHhQSEhIg5+zstV72LBhrALo6OhIw4YNY5Xu5ORk\nIiKlazQ1MFq4cCHxeDxavnw5FRcXqzXvp0+falzRrSqvMQq5psr34MEDth1NmjSJBgwYoDTYU7cy\nrsoCvmXLFrKxsVGrMbUykyZNUlLEzc3NKTY2lp3N8vDwUEpvzHtUGalUSrNmzSJ9fX0qKSlR0rGa\nQhn/7bffiMfj0ebNm9nvcENpFmVcLBbT6NGjafTo0VReXq5RC5dcLqe7d++Sq6sryeXyRlfYu0xY\nWBhNnDiRLC0tKTQ0lFWExGJxs1i4nJ2dadWqVU0ul4joyJEjxOfzycXFRWODIJlMRjKZjEaPHk0M\nw5Cbm5vaO3SxWKxy1ujRo0e0d+9e9u+7d+8Sn88nhmHo008/VYvswsJCevToES1YsIAePHhQp3uS\nkpKUXIQUU4aNQSKRUN++fUlHR6fO5dAUcrmcAgIClD4sqampapUhEAjI09OzmtXWy8uLYmJi1CpL\ngULxU1hrFQqKn5+fRuQpKCgooLlz55KOjg55eXlRYGBgnZXCHTt20JQpU+os6969e2RnZ0dLliyh\nyZMnv9VFQCaT0fr165Us5Z06dSKxWFxnmZUJDQ0lR0dHdqDj6OhIoaGhNV5bWVl3dHRskMyakEql\nFBERQWZmZsTj8ejJkydqzb+qUqxuo8zbeJtiriiPr68v+fr6arQsu3fvZtuPo6Njtfda3bPHo0aN\norlz5yqd8/b2pm7dumnE6Onn50d6enpsf2hsbExPnjyh1NRUsrGxUbKYjxo1igQCgdrKcebMGTI2\nNlbZLyre8cGDB6tFlipOnTpFDMPQ77//Xi1NJpPVS7dtFmW8qKiIGIYhZ2dn1rqoCeRyOQmFQnJy\ncqLevXtrREZdUIdCUhsKf93333+f3rx5Uy09MTGRQkJCaOrUqZSXl6fRslQmKiqKgoODm0weEVFq\naiqlpqayVvFz585pTNaBAwfowIEDxDAM6ejo0LVr1zQmS0FRURHt3buXwsPDWeuwTCajvn37si5J\njfVXVwyQp02bxirVw4cPp/z8/Drdv2vXLva++/fvN6osEomEJkyYQAzD0IULFxqVlzq4ceOGkiLe\nq1cvEgqFapWh8BVXdWhi5oOIlBTwyoem/MbFYjHt27ePDA0NafXq1fXulwICAsjT07Pe1tyoqCi2\nLg0MDOjUqVNvvSc7O5u++eYbdrA7ffr0esmsLwKBQElh9/PzU+ugSCKR0K1bt5TasZWVFVlbW9Oi\nRYto0aJFjWrTVZXg+ii7CiVacTTWuFHVOl+Tb7ivry+NGTNGI4OGDh061Pg+Ozg4qN1YFBsbS3w+\nn3Jycthz3t7eGrGMC4VCcnZ2ZtuRtrY2JSYmklwup59++kmpjWlpabFKuFAopFWrVpGXl1eD3RkF\nAgE5OzvTgAEDqqXFx8eTlpYWu7ZI3ZSWllJpaSl16dKF9PX1VRo7p0yZQt26datzns2ijD98+JAY\nhqH169c3Oq/aKC8vJzMzM1q5cmWdFQlNMG/ePDp69KhG8r5w4QKdPHmSTp48qXK0KRKJaO/evbRs\n2TJiGIZu3rypkXJURSqVNvkshFwup6NHj9LRo0eJz+eTpaWl2v08iYhyc3Np6tSpZGFhQRYWFsQw\nDIWFhaldjip8fX2pZ8+eSh3Y4cOHqVu3bsQwDI0ZM6bRg1uRSEQikYj69etHo0aNoqVLlxKfzycv\nLy8aNWoUPXz4sNb7Fcp4u3btGu03vmjRItLS0qJdu3Y1Kh91UFxcTN7e3uzHZe3atWp1SRIKhSQU\nCqlt27bsx7pVq1YUFhZGaWlpNGrUKHY9hibYunUrAaD58+crKefqRjEz2rZt2wZZBRMTE2n+/PkN\nmlGtrIxfuXKlXvcOHjyY9TdPTEzU2Donxe+g+C3Uzbx586otQFb4MysODw+PBhuRGuL6UZvS3FgF\nubKCr6psVWWr01IuFovJzs6uRmVcE2sBXr58STwej/UXJ6pQxtXtMy6TydjFi4pD0V4vXLhQrY0p\nnlUmk9Hs2bPZ86oMiHVh//79xDCMSoPP0qVLiWEYsrW1Vbv7FRGxLps8Ho+2bdtWLT03N5cNdlBX\nmkUZHzx4MJmYmGjcSlteXk4Mw2hkYUp9mDFjBm3fvl0jeZeVldXpuoiICLpx40aTRK4pLy+nzZs3\n07179zQuqzLXrl1TWkhYuTNSJyEhIRr1+auNVatW0dixY+n69eskkUjoxo0brG/c5s2b6zVtXxMK\ny3hJSQlJJBISiUS0fft2GjFiBPH5fOrdu3c1hVARFenEiRNslBuF72tDmTt3LgGgpUuXNiofdSAS\nicjHx4f9gPTu3VvtM15xcXEUFxdHhoaGZG1tTWFhYZSVlcWmK4wYo0ePVqvcmtCEMi4SiWjo0KE0\nZcqUBlsExWJxgz6wWVlZZG9vT3Z2dnTo0KF6D6S2bdvGvvOnTp2qk1W9PiQnJ1dbzFmTG0tDkEgk\n5O/vTwDY5xg6dCi74DwjI4ONRsUwDJ04caJBcuobmaQpI5xUVfIVi0s14U4jFovp8OHDNSriY8aM\n0YiL7suXL5W+f8XFxdS+fXvi8XhqVcbPnTunpGxPnDiRfaeWLl1aTRmPj48noVBYbTDYUGV848aN\n1K9fv2rGp5KSEnJ3d6ehQ4fS5MmTG/2cqti2bRtt27aNeDweG1RBQXJyMlvfPB6vzn1Vkyvjubm5\nNHTo0CbxJY6NjaXTp0/T48ePNS6rNp4+fUopKSkNbnQNQSwWU0REBF25coX9V9PhHGUyGUkkEtq0\naRN98cUXTe6nPmPGDFYR79Chg0bcnxITE+m7776jL7/8kl2trqpDLS4upvv371NiYqJay/Hw4UOa\nPn06OTo6klAoJJFIRIWFhdSlSxdq27atRt2CUlNTafTo0WwdBwUFEVHFAul27dpRu3btWIv46dOn\nG/Wh2bx5M7Vq1YoCAgLU7gbSEBQLdXg8Hunp6Wk0MlJKSgplZmZWO5+RkUEMw1CHDh00JrsymlAI\nb926RW5ubtVmTEpLSzU+e3nz5k1ycHBosI//s2fP1K6MCwQC2rp1a7XFmupetKlwC+LxeKSjo0Pj\nx4+nJ0+eKA1IJBIJzZkzh+bMmUM8Hq/BoXArK7i1KbeqrNWaVsZrk6cui3hZWRmVlZXRihUralTE\n/fz8NGKxVRAeHs66qSgs5dbW1mp1U1m9erWSUu3s7EwJCQkUGxtLTk5O1ZTxyZMnVwsZa21t3eD+\nfc2aNbRgwYJq57ds2UKtW7emGTNmaEwZ19HRIR0dHTI2NlYKJpCUlET29vZKz1jXdU416duN3oGT\ng4ODg4ODg4ODg6OBaNJNxdLSkiIiIhqdz9s4ePAgzZo1S+Ob3dRGeno6RUVFUUlJSZ1dShqKwgIb\nFBREqamprCX85cuXZG5uTunp6RqRm5ubS6tXryYvLy/i8Xjk6OjY5JtGvHr1it3kgM/nq1zdrA62\nbdtGe/furXWKPTExkWxtbZWsIOqmsmX27NmzxDAM9erVS2OLoRUorGd8Pp/09fXp0KFD7GJZPp9P\ns2fPrrcvripOnjxZZ3/TtLQ0jVlVs7KyKCsri1xdXVmr+OHDhzUi621ER0f/4y3jn332GR04cKDa\n+enTp5OpqSlNmzaN/P392QVS6lx3cvPmTRo6dGiD1zGUlZWxfvvqsIwnJyezCzVrOxTRVOrr9qUI\n9XbkyBEla90XX3yh8vri4mIli15D3cxU+X9XXpSpOFS5iVQ9NLGgsiZZ6oiEVVZWxm4+o+j/hw8f\nTlZWVkqWcU2Gcq6Kwm1F3W4qVS3j9T0WLlzYKLfZ/fv30/vvv8+GjT5//jydP3+ebGxsKDIyklxd\nXTViGS8sLGTbzMKFC4mowitg27ZtrAvYd999x/bXjbWMa0QZF4vFdP369SZxUcnIyCA+n0/m5uZN\n2vCr8uzZM40umhSLxRQWFkZhYWHsLn1FRUVK4bc2btxIq1ev1lgZKsdNHTp0KM2ZM4ciIiIoLS1N\nIzskVqWsrIxmzJhBANiOsLkICQmhHTt2sLGJtbW1Nbq489mzZ6Svr08A1B5iryYSExNJX1+feDwe\ntWrVijw8PMje3p7s7e2bNFpPWVkZzZ49m3R0dGjUqFGN6pskEgkbE1dxhISEkJGRERkZGbEfECMj\nI/riiy9qPN62uLUxzJgxgxiGof3792tMRmUU/svqXETo7e1NXl5eKtMEAgFdvnxZKb73zJkz1eaq\nERERQadOnWpwnxQTE0NWVlZkbm7e6Fj3AoFApWuKInpKaGgo+fn5VVPW6zOwX79+Pa1fv15JAXJ1\nda1xwH7o0CH2ukGDBjXKoFJ5s5+GHJoMN6gpP3G5XM5u7qM4tLS0KCUlhY12pTg0ERGpJhRuKupe\nwJmdna3kG12fY8GCBY02TgYHBxOPx6O2bdsqbeDl6+tLcrmcXFxcNKKMV44bv2XLFiotLaUlS5aw\ndTx58mQqKyujp0+fqsVNRSPKeEpKCjEM0yS+0+np6cTn88nCwkLjsmrjyJEjTRZpQxVZWVk0Y8YM\nevbsmUbyF4lE9Omnn9K2bdtIKpWysbfLy8spKCiIgoODSSKRaHRA9NtvvxGfzydDQ0O6c+eOxqJN\nEBE9f/68xo/ZpUuXqEuXLuyL2qFDB1q0aJHGykJEtHPnTmIYhqysrJps0NmuXTvi8XgEgExMTGjf\nvn3sAk51kJqaSlevXn2rL6GXl5fSB66hFvnnz5/TjBkzGmzh0dLSIgMDAzIwMKCzZ882qAxv482b\nN2Rvb6/RaCpV0URElezsbNLW1qavv/66xgWwMpmMoqKiKCoqirWYNzYOdlRUFF2+fJlevXrV4DwU\n0VSsra0bdL9AIFC56U9lRVzVwEMR2lBxb11ZuXIlrVy5kvX/VuwUqEqhDw8PVxp0Vg6L1xgqRzKp\nSTmvbC3XNJq0vJ87d66ab/hvv/1GRBX7QqxatYo9P3HiRLXJfRuasowTEeXk5NDcuXPr3Ff6+PjQ\no0eP1DaDGxcXR4GBgUpHSUkJJSYmsoqxuqmsjM+ZM4edNeXxeLRx40Z2kPHOKuMZGRmsMt4U1lKi\nik1ntLW1NRLe7m1kZGRQRkYGLV26VOMLJ2tj3bp15Onp2SyyR44cya6W1tQA7PXr12Rqakp8Pl/l\n9Le6GTt2bDUXFaFQyG5dXjl+rFAo1Gj0mtLSUjI3NyeGYWjjxo0ak1OZjIwMGjduHNu5a2trqzWa\nzI0bN8jExIQuX76scnBRVFRECxYsoAULFihtKb1s2bIGWVqSk5PJ2Ni4wYr4pk2bmiRcqGLQ5e7u\n3mS7j1beeEadXLp0iSwsLMjS0pLWrFmjtLupKrZv3052dnaNWjR75swZWrFiRYPvT0tLI1tbW7K1\ntW3QfgKKzZQUu2xWtXhrIp67QhlnGIYiIyMpNzeXGIah1q1bs3UpkUho3rx5BIAd9Kh7A6B3AcUu\nq5qywEskEnJ1da2mjFdepHnx4kX2fFPsSaGgsmX8559/Vnv+crmcjcA1depUpf5xzZo1tGbNGhKJ\nRFReXt5ku59fv36dGIah69evqz3vpKQk9ndUPOcHH3xAp0+fVrpOEd6xrrpPTfq2Vv08zN9OZmYm\niouL0aVLF/B4TbM+NC0tDVpaWiguLlY6L5VKIZfLoaurqzHZMTExAIDvv/8e8+fP15ic2ggODsbm\nzZvZsjQ1/fr1g6WlJQwMDDSSPxFh27ZtEIlEAAAfHx+NyAEAuVyOU6dOoby8HNra2igvL0dISAgA\nYPHixXj+/DkAQF9fH0uWLMGGDRugo6MDY2NjjZUpLi4OBQUFsLOzw4oVKzQmBwCEQiEiIyNhaGiI\nP//8kz3frVs3LF++XG1yvvrqK5w/fx6DBw+ulpaVlYUePXogMzMTAKCrqws3Nzds2bIFvXr1go6O\nTr3lde7cGRKJpNZ0AFizZg0++eSTauk6OjpgGKbecuvDq1ev4OvrCy0tLRw5cgSmpqYalaegS5cu\n7P/DwsLQt29fteQ7cuRIvH79Gn5+frh+/Tq6d++O0tJSjB8/vtq1L1++RFhYGNauXQsjI6MGy5wy\nZQoyMjIQHx+PTp061evekydPYtmyZcjPz8fGjRsxfPjwBpWhV69e6NWrF4CKdq5g69atWL16dYPy\nrI20tDQAAMMwuH//PpydnTF16lQEBQXh2bNnKC0txZIlSxAdHQ1HR0fcuXMHdnZ2ai/Hu4Czs7NG\n87927RoePXqkdG779u3Q19dXeX3Xrl01Wp6qKPqouLg4TJw4Ue158/l8AICnpyfOnj0LANDT08Pn\nn38OAI16dxuCQp6JiYna837vvfdw7do1AMDFixfRqVMnLFiwAHp6ekrXJSYmAgDMzc0bJ1DdlvGI\niAgyNDTUmLuEKvr160daWlpK24YTVWx6sGTJEo3KDg8Pp/DwcDp27JhaF0DVlcLCQvL29qZRo0Y1\nuWyiCl/21NRUjVrx7t27xy4cXLlypcbklJSU0Pr168nZ2ZlEIhEFBwdTr169lCwgXbp0oYMHDyrF\nhdY0x44do7lz59KcOXM0Pvtz7Ngxatu2LZ04cYJMTU3J3t6etm7dqtYZj7i4OFqwYIFK9xSxWMxu\nXKE4du/e3WiZvXr1og4dOtD8+fMpPj6enVpUHJmZmSrDDDYVubm55ODgQAzDaGw3zNpAJcutwpde\n3RQXF1NaWhrduHGDbty4QStWrGD/f+PGDcrKymr0Qs59+/bRjh076jU9LpPJaN68eWx72759e4Ms\ne4o6Cw0NZRdjKup0/vz5GgsBm5OTQzk5OdSzZ0/i8Xjk6+tLU6ZMqbbBz6RJk946O/FPRtPhDIVC\nIVlbW7PtxMzMjMzMzJQ8AAoLC8nNzY2d3WoqCzGRsmXczc1No7IOHz7MtitPT082zGNYWBi7u2tT\nzOIuXbqUADSri/DmzSgGC28AAA9YSURBVJuJYZg6X98kbip5eXk0cuRIGjRoUJPuyigUCmnWrFnU\ntm1bpZXvfn5+Gt1aOyYmhlXGieq+MY86CQ8Ppw4dOjTZlHZlAgIC6MmTJxr3YT527BirjKt6zrt3\n76pFzo8//kgMw5CzszO5uLjQ2rVrqVevXmxs7VmzZjV6C/r6IhaLac+ePWRlZUWtW7fWmOuXSCSi\nyMhIioyMpDdv3pBAIKB+/fpRly5d1K5EeHh40OHDh6u1G5lMRp6enuzHTlHvsbGxjZYpEonYSCxi\nsZhdiOPs7Ez79u1jp181TXl5Od2/f5/u37/PPn96ejrNmTOHHew1xy7Cld0pFC4W/0RKSkooNja2\nzhs1paSk0OLFi5U29qqvn6tAIKDQ0FAKDQ1V2lUT9VyI2ViuX79Ojo6OrJKkUMadnJzoyJEjzbo7\ndVOgaWX8+fPnSkaCZcuW0bJly5Su+fLLL9n0//73v42WWR8q+4y7u7trVFZubq7SQG/o0KE0dOhQ\nMjExYc81hYFQsQNncyrjW7ZsIR7vHdqB8+eff6aAgAAyMzOjwYMH1/d5GoVcLqc9e/ZQp06dyNra\nmpYsWUJLliwhBwcHjWwzrGD27Nk0fPhwGj58uMZk1IZUKqW+ffvSvHnzmlx2YWEhTZs2rUkUmNmz\nZxOfzydfX1+SyWQklUopODiYgoODyc3NjeLj49UiRyQSsRFjFi9ezA6uFO+DJjdvqImSkhJasGAB\neXt7qyWUYE0EBgbSuHHjlCz+ixYtopEjR6pdGbe0tFQ5gJJIJGx0mlmzZmlMQY6MjGQ/GEePHlV7\n/rXh7+/PfqxdXV1px44dbHhMbW3tZtu4rOrW7JrsNzXN25TO/Px8SklJocGDB1OrVq2ULOINWXA2\nf/588vPzo2HDhrFRUpqLkpISOnfuHK1atYpWrVpF33zzTYN3QP2nUVtUF3Uo44roWYrjwYMHSov2\noqOjydjYmBiGIUNDwyb/Xigs4wDUvoCzKuXl5bWusdHR0WkSw9XSpUvJ2Ni4Wdv4O6eMJyQkEMMw\npKenR3FxcfV9nkYjl8uprKyMXr58Sffu3aN79+7RyJEj6eDBgxqRV1RURL6+vnTt2rUmXaRRmcLC\nQoqJidHoLoE1ER8fX217WE1ha2tLfD6fNm7cSJmZmdSrVy8yNTUlU1NTOnnyZJNOBTYHaWlptGvX\nLo3GFo+Pj2ddUeLi4sjT05P8/f01FrO+ORk9ejQb6aM5Fn23b9++2kLgNWvWNMvsloLKizj/6cr4\nrVu3aM6cORQdHU3Z2dl0+PBhunDhAmVnZ1N2djZ5eHiwEVO0tbWpd+/eFB0d3eB+pLLS19CY3RyN\npyZFHGqKLV55Yaa2tjalpaVRWloaEVW4YFV2YVm6dGmj5dUXiUTCRjzRtDIul8vp3LlzNSrjil2b\nNc27YBmPjo5WizLO7cDJwcHBwcHBwcHB0VxocgfOfzuzZ89mdxdLSEhoUtmJiYlNsrtpZcRiMSUn\nJ9OBAwea1Bq/fv161mdc4RO3du1aWrt2bZOVobm4evUqmZmZaSTmtCK82bZt29hz0dHRtGHDBuLz\n+XT//n21y3wXGD16NHXq1Km5i/HOAQ2F3msOvL29ydzcnPT09KqFoVMcbm5uall0r3BT0dQCTY66\ngWbYWOhd48qVK2RtbU23b9/WuCy5XE6LFi1SsoivXr2aBAJBk60ZfBcWcIpEonp9T2rSt5m/tvxU\niSbCxXA0nsLCQqxYsQIrV65E+/btm0RmWVkZZs6cCWtra3z22Wfo1q1bk8gFgJKSEgwcOBARERHw\n8PDA9u3bWfna2tpNVo6mJCMjA8nJyUhMTISRkREmTZqk1vxlMhkmTJgAAHj9+jXu3LkDANi9ezdO\nnToFf39/WFhYNFl4Ug4OdZKfnw8tLS0sW7YMP/74Y7X0Ll26IDg4uMnCR3JonoMHDyIwMBAA8Ouv\nvwIAnj59io4dOzZnsZqUpKQkODs74+eff1YZQvTfxo8//ogzZ87g6tWrNYaXfNcQCAQqz3PK+D+Q\n5ORkDBgwgI0vy8FRX8rLy3Ho0CEAFfFjIyMj8dVXXyEjIwOenp7Q0lL7FgQcHBwcHBz/X8Mp4xwc\nHBwcHBwcHBzNRE3KeK3mr5pu4uDg4ODg4ODg4OBoPJxDKAcHBwcHBwcHB0czwSnjHBwcHBwcHBwc\nHM0Ep4xzcHBwcHBwcHBwNBOcMs7BwcHBwcHBwcHRTHDKOAcHBwcHBwcHB0czwSnjHBwcHBwcHBwc\nHM0Ep4xzcHBwcHBwcHBwNBMaUcZ5T5/CyMMDLWxtNZH9OyfXxNQULVq2RAsrK/YwHDpU43L50dEw\n/PhjtLC3h3G7djCY8P/au7/Ypso/juPvc07Xdi3t6mZcGP/WiSEREcTJMISIFxpv3HQzXYxINCZ6\nAWGJzMUEDKJzyUiIccmmxhgDIWESRLkTw4VhLOCQMGRbzEAmUxamgnWlW7v+Ob+Lsrn9wCv2nGdh\n31fSi/Vin/N98pzv8+z09CyC2d+vPFdbvZ2d0zInXgWhENaJE0qzdc2tqdytrblaOzqUZ5nnz+Or\nriYQDhMIh/HW10MyqTwX9I21ceUK+a++SuCBBwiEw/hqazEvXlSeq+t8Aj1jrbNeHdk6+xaAu62N\nwEMPEZw/H/+TT2L98IPyzGn5DvatubhG6OrVZn8/vkgkl1tWhq+mBrOvT32uxrVpgoo5PeOb8byv\nv8b/3HNk7r9/pn/1rMydED98mJHh4clX/OhRtYHRKP6qKtJPPMHIhQvEzpzB9vnwbdyoNvcmx+sF\nMuvWTcscGR5mdO9eMqWlZMrLleXqnlsAxuAgntZWZ8KiUfw1NWTLyoidO8eNEyewenvx7tqlPFrn\nWPtffBGA2OnTxM6eBbcb3yuvOJKt43zSOdY66tWVratvAeTt24enrY34/v2M/PILqeefx9vUBNms\n0twJjvYt5uAaoatX2za+SIRsSQmx3l5iPT1kFy3CH4mA/Z//1P3OaVybJqia0zN/ZTwW48bRo6Sf\nemrGf/WszNXESCYZa2wk+eab4PFAKEQqEsHq74dEQvfhOSMeJ7++nsTu3eD1qsuZBXMrf9s2km+8\n4UiWq6sL46+/SLz3HgSD2AsWkHj/fdz790MqpTZc11j/8w+ZFStyNYdCEAqRfP11rJ4eiEadPRan\nzIJ5PSc51bcAz4cfkmhoILtqFfh8jG/dSvzIETCduUPVyb51W3f5GqGrVxvXrmH9+iupSAR8PvD5\nSNXWYv7+O8bffyvL1bo23aRqTs/4GZnatAl7yZKZ/rWzNneC55NPmPfIIwQXLsRXW4sxOKg0zy4u\nJrVp02RTNS5fxv3ZZ6QqK5U3eHC+3tseQ0sLmWXLSD/9tNIc3XMr79AhzKEhxjdvdibQtv99Tbx1\nzz0YIyOYAwNKo7WNdUEBY62t2IsWTb5lDg5iB4MQCCiP13E+6ZzXOvuH7t7lVN8yhoawBgYgm83d\nPrF4Mf7KSkduZQQNfes27vo1QlOvtu+9l/SaNbj37ctdrBgdxX3gAOm1a7ELC5Xl6lybQO2cli9w\nzoB0eTnpNWu40dFBrKsLMpncxzXptPJsY3Awdw/kypUQDDLa1qY8U2e9k6JRPB9/TPLtt53L1CEa\nxbtjB2MtLeByORKZrqjALizEu3MnxGIYf/yBp7kZ2zQxrl935Bh0M377De+775KorwfLUpo1K84n\nB+msV/tYO9i3zKEhANzt7Yzu20esu5tsURG+2loYH1cbrqFv3e4Y7vY1QmevHt27F+vsWQpKSyko\nKcE6dYrRTz9Vmql1bVI8p2UzPgPix44xXlcH8+Zhl5QwtmcP1s8/Y/34o/Jse/FiRv78k5Fz58C2\n8VdWKl9YdNY7wfPFF2QefJDMY485lqlD/o4dpCoryTz6qHOhoRCjX36J1dNDcPly/FVVpKqqwDAg\nL8+549DE7O1l3jPPkHr2Wca3blWeNxvOJyfprFf3WDvat25ePUxu2UI2HMYuLCTxwQdYAwNYZ84o\njdbSt/7PnFgjdPXqVAp/JEJ6/XpGLl1i5NIl0hs24K+uhrExdbka1ybVc1rTn6x3N3vxYmzLwhge\ndi5zyRJGW1ooKC3FOnmSzPr1zmVrqDfv8GHGIxHH8nSwOjpwff89sZMnHc/OlJcT//bbyZ+NwUGM\nTIZsSYnjx+Ik6/hx/C+/TLKuLvd9DA10nE866azX6Wwn+1b2vvuA3Mf4E+ySEmyXC/PqVTKKcnX2\nranmwhoBenq16/hxzN5eEt99B/n5ACQaGwl+/jmujg6ltwXpqNeJOS1Xxu+Q2d2Nt6Fh2j1M5sWL\nuckRDivLdX3zDfPWrp2Wa0w83kfhX4i66p3KuHwZ6/x50g49Dk0X94EDGNeuEVi5kkBZGYGyMgD8\nL72E96231AUnk+S1t0/72C/v2DEy4TD2/PnqcjWzzp7Fv3EjY3v2OLYRnw3nk5N01qt7rJ3uW/aC\nBdjBINZPP/17DFeuYKTTZKd8N2KmaetbU8yVNUJbr06lbrl3m1RK/VN6NNXrxJyWK+N3yC4uxt3e\njh0Mkty2DSMaJb++nvTjj5N9+GFluZmKCsyhodx9rQ0NkErh3bmT7MKFZBTm6qp3Kqu7G9vjIbt0\nqSN5uow1NZHYvn3ae8HlyxltaSG9YYO6YLcbz+7dWKdOkWhuxrxwAU9z8y3HclfJZMjfvJlEfT2p\nF15wLHY2nE9O0lmv7rF2vG+5XCRfew3PRx+RXreObGkp3nfeyd26sXq1slhtfWuKubJG6OrV6bVr\nsYuK8O7alcsyTbxNTbkvdlZUqAvWVK8Tc3rGr4zPKy8nWFxMfl0dRjw++dD9vPb2mY6aFbn2/PnE\nDx7E1dlJcNkyAhUVZIuLGd2/X33ukSNYp08TXLqUwOrVGNevEz90KPeoIZW5Guqdyrx6NffRq0OP\n59I1twiFcle3prwA7KKi3OP3VDGM3JdzensJhsP4a2tJbtmSe3qPYrrG2urqwurrw9vYeMs/DLE6\nO5Xl6jyfdIy1znp19y6n+xZAcvt2UjU1+KurCS5bhhGLET94UO0x6OpbU8yZNUJXrw6FiB8+jNnf\nT2DVKgIrVmD29RH/6isoKFCXq7Fe1XPaiEajCp/QLoQQQgghhPgvcs+4EEIIIYQQmshmXAghhBBC\nCE1kMy6EEEIIIYQmshkXQgghhBBCE9mMCyGEEEIIoYlsxoUQQgghhNBENuNCCCGEEEJoIptxIYQQ\nQgghNJHNuBBCCCGEEJr8D2TBGgETuYL3AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7f60c557e908\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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ZMG6QVlZWiuyC1RylpaXySvERI0aY1SQa869uxh8/fgxLS0sIgtAovzprysvL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\n/wCsEZOSv+yRXQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7f60c53b3048\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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3b65z47lv3z6sXr0aHMeJZjw7OxvXr19vdPmlpaXguOrVaWuPHTMYDDh27Bg4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\nQyH+P1XlOjLj84sUAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7f60c531dc50\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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IfuSCIGDlypVtvqam+PLLL5m1sq3UtarMmjULw4cPZ6Wxpb0nT55g+PDh9XzGpVLGCwsL\n4e7ujo0bNwIA5s2bx/4vBVVVVejatSsEQYCPjw+WLFmCe/fuNfkbvV6PDh06SK6MHz58GIIg4NNP\nP23Qcjhy5EjJlvCLiopMluAuX75c75icnBwMHz6c9fO8vLw2y20OWq0WW7ZsYa5BRIQOHTpIrhTv\n3bsXarUa27dvr6eQnzlzBmq1GgsXLpR0o1VTVFdXM0VcqVRi4sSJksu4ePEiMyA0thpiSSIiIiAI\nQpvd71auXImAgAD2HOjUqRMWLlzIlNaGuH//Pnbt2tVqZby0tNTEDURUTmbOnCm7glTXMr5+/Xr2\nXXx8PBwcHNgD/OnTp7LWpSGWLVuGZcuWQRAEfP/992aRefr0aZPn0Z49eyQ7988//wxfX19s2rQJ\nmzZtavCYxMREKBQKHDp0SDK5APDtt9+yeXvIkCEW3T9VVFSEDRs2MIu4QqFARESEbHPm1KlT4ebm\nxnRGvV6P48ePQxAErFq1ShIZ58+fZ+177Nixlx7v5+cHQRAwcOBAyQINpKWl4eLFi+xF4OLFi5g1\naxacnJxYOyckJDTrXBZRxtevXw+FQgEHBwcsXrwYixcvlsQfLyUlxcTnT/w3NDS03jKfXq9H7969\noVAoZPEdMuarr76SzIpUVxmvq1g39Bam0+kwatSoBn8zbdq0Nj/oDQYDYmJiEBAQwKx3UivjAPDp\np5/im2++afYDMy4uDoIgICAgQDJLbVlZGTp16gSFQoFvvvkG33zzjcn3BQUFsLGxwaxZsySb6Iwt\n40OHDjWZ1HU6nclqgTmWs/Py8rB69Wq4u7tDEARYW1tj/vz5mD9/vmTL+SKVlZUIDQ1l7hN1J9F1\n69ZBEATcvHlTUrlN1WfmzJls/AwbNkzyjVDinhC1Wo3NmzdLem4pKCoqgp2dHezt7Vttrc/IyIC3\ntzfrtwEBAc1Swnbv3m2yJN0aZXz+/Pns/qlUKpw8eRInT55szWW0mIqKClRUVGDcuHFs/r1x4wbi\n4+OZD7m9vb1JdCQpKSwsxNq1a7Fz58563509e5a1a0REhOw+v/v378f+/fthY2PD7kdbVvXq8vPP\nP6Nnz54YPXo0KisrG3wGiJsqX3/9dclWnvbt24d9+/YxI4qfn5+sq1qNodfrUVpaiu3bt7Nnlo2N\nDTQaDTQajWwbOIuLi+Hh4VHPXaS4uBiCIGDJkiVtlnHp0iXmctKrV6+X9pkbN26wwAYHDx5ss/zS\n0lKUlpaie/fuTUZUSUhIaPZKgNmV8fXr10MQBLi7u0u+ebK8vJz5rIrRU6KiohqVY3ysnIjKeN++\nfdt8LlGxbmhDhnEHyMrKQlZWFjQaDQYOHFjP5zIgIMBEeW4LT548qfd22rlzZ8mV8ZYSEBAg+WbC\npKQkdi/FzVjGXLhwAUqlslG/tdaQkZGBwYMHM984b29vthwnuoY05U/eVsSwW3v37sX8+fNhb29v\nohCJLhRyPHDOnDnD/MFDQkKQlpZm8r0YwtQcG3QrKysxe/ZspjiEhIRIGuFCjGIjPjjNvQm2uYj7\nCubPn9+q3589exaenp4s4tXhw4ebfMEuKirCnTt3EBgYyCJG+Pj4YOfOnS1+4d28ebOJa8qJEyca\nPO769ev46quvTIqUyktVVRUGDRrEIrY4ODiwOs2YMUMyOcbcuHEDzs7ODUaGycvLg4+PD5ycnODk\n5CRrNKSSkhIcOnSIuTCI1z1q1CjJQkfeunULnTt3hr+/f6NW0NLSUowcORK2trbIzMyURO7NmzdZ\n9BvRNcXcFvFLly4hPDwc/fr1q6cfNLSyKjU5OTkNygkMDISPj0+DwSVaQm5uLry8vNiz8GUrSNXV\n1Rg2bBgEQcCIESMkMZLVdTdrrAQFBTX72WRWZXzYsGHMmiTXw7O8vLzZG17EpU6lUilLXUQePnwI\nQRDQoUMHSR7edaOpGFvGmxMqS6FQNOpi0VIMBgPCw8MxaNAgE0XQ3t5e1g0SL+Phw4ewsrKCIAiS\nhccqKCiAra0t1Gp1gxtvtFotvLy84OHhIXn/vnfvHu7du4cePXo0uFlGqs2LqampLIzikCFDMGTI\nkCY3yYpFjFs/c+ZMnD9/XhLFRavVsnjm169fx+XLl02sDEeOHIEgCCZL/XIybdo0NobefvttSf3i\nS0tL0aVLF3Tp0gWCILQ4JKU5EOd/0aLdWn9iLy8v9rCKj4/HF198gS+++AJXrlxh/9+xYwd27NiB\n4OBguLq6moRnS0xMbJXf6Q8//MDOY2tri1OnTgH4n83Ha9euhbOzc4PRvhQKBVQqFVJSUlp1zXUp\nKSlBYmIiizrVmA+5VKSlpcHFxQUdO3as9wzS6XRsH1VycrJsEXKA2usW4zPXfW5Jde0VFRXo0qUL\nunfv3qjiJypoNjY2OHHihCSrAEVFRSahC319fRt1GSwoKGBRctqymimGhv7www+Ze2RTxdvbGyNH\njsTIkSMRHR2N6OhopKamyvbyZTAYYDAYkJSUBFtb2zavUoseDYIgoFOnTnj27NlLf6PVatn1X716\ntdWyjRFXuKKiouDi4sLG7+uvv45hw4aZzBvNNcKaRRl//vw5li5dCisrKwwePFjyzV2tJTc3l02E\nkZGRsskRl/9cXV0lUdKMfexaq4xfvnxZkrfkFy9eQKFQ1HtI2dvbM59ucVOQORBliVbxGTNmSLbc\nKm7MdHR0xJYtW/Dzzz+b+KInJyfL3pe0Wm2DEU+kstCKIeuMixgicsaMGdixYwdu3bqFmzdv4tat\nW7h16xb27dvHJnjx2B49ejQYaaYliLHD64aVE5ecRT95qaxadamurmZh7/z8/JiSNnHiREl9Lauq\nqhAeHs7ae8aMGfUsxcXFxTh+/DgmTpyIPn36QKPRSOLfXFNTgx9++IFtpE9ISMDdu3cbXOUQX+AF\nQcCUKVNaPa6MlfG6ljvxb3t7e9jb27OlaIVCgZCQkFZH9igtLYW3tzebA0VDRHR0NDp37ozOnTub\n+JDv378fP/74Iy5evIjx48fDxcUFSqUSXbt2lSS6iE6nw+nTp5mPeEM+5FIhus45Ozs3uC/r6NGj\nUCprc1TISX5+PovDbPzC4+LiIpkMg8GAjRs3QqlUIjU1tcFjampqsGjRIghCbaz8yspKZGdnt3mv\nzdChQ5nbnrW1NdLT002+f/z4McaNGwdra2vmMiEIApRKJVasWIEVK1a02Gqck5ODnJwck/Hj4+OD\n/v37IywsDHv37sXevXuxc+dOrF+/HsHBwSxfgL29PfvNpEmTJA8lqdPpsGjRItbWAwcObLNhTDRs\nCoKAbt264dSpU7h06VKTv8nKymK/keNFMzs7GxkZGcjIyEB5eTn0ej2+//57yTZwKsyc8JPD4XA4\nHA6Hw+GISGUZP3v2LPPrXLt27S/GKi4iWiWkzsxpjOgzLghCi5OwNMTcuXPbbBmfMGECJkyY0Oa6\nHDp0CH5+fvWWuezt7ZGeno5t27YhJSVFsuXdl3HgwAEcOHCAhTBqq3+aMWIECfqn9bcxC7Lozypn\nQpS6sjt06ID9+/e3eQXi1q1bLEzhjBkzcO7cOZw7d65Fy5hHjhyBvb091Gq1SYjRllBZWckiIW3f\nvh3l5eXMd33r1q3YunUrBEGASqWSNNyqMXWTkCgUCkybNk3yiBuZmZnM0tOtWzcTy+vz588xYcKE\nBjP3DR8+nEXnaAkGgwEPHjzAgwcP0K1btwb7spubG+Lj49l+m/T0dGbxc3JyalN8ZKDWr3XPnj1Y\nuXIlNm/ejLNnz+Ls2bNsT4C46iTWZ+jQoW3yJ37x4gW7j2PGjMHz588xfvx4qFQqk3s8f/78BmOX\n37x5E/b29lAqlZJsQCssLESnTp3qzc+dOnXCtm3b2nx+YxITEyEIAr766qt6312+fBl2dnZYsWIF\nDAYDS9ii0Wgk24xdVVWF+/fvo2fPniZjad68eZg3b56ke8cKCgrg6OiIsLAwALVWy8zMTFauXr2K\nJUuWmPR1tVqN4cOHt2nV+tatW8za/d577+G9994z+T4qKgodO3Y0kTt79myTTcyCIGD69OktkiuG\n8Y2Li0NcXBxycnKa7cKVn5+PxMRE5gamUqkQHx/fIvkN8ezZM+zcuZNFMhMEATExMZK4whgMBowZ\nM6befCXOTdbW1ggLC8O5c+eYq+SmTZsgCAK8vLzMloiopKQEHh4eUCgUsLa2bnDs1UU2N5Wamhqk\npKSwhjp37lxbr08WxCVQf39/2eKNGyvjISEhePr0KZ4+fdoqv9ri4mK2/DF27FgA/zMgExMTkZiY\nCF9fX/j5+cHPz49lJMzMzDRZyhJDIbaVZ8+eQRAEDBgwAHv37sWPP/6I2NhYWFtbw9PTE1OnTjWb\nm4per0fXrl1ZCMQdO3ZIev6CggLs3r0bGo0Go0ePNim2trYmS+5+fn6ybGAVfS5FWUlJSSyOvSAI\nbQ4PKhVXrlxhdWoNe/fuZW25ZMkSE99a8bwKhQI9evSQuOa1rFmzBm5ubiaK2rlz52QJfTd69GgI\ngsAUUgB49OgR3nrrLdjZ2cHR0REHDhzAyZMnUV5ejtTUVNYHHj9+jMePHzdblsFgwBdffMHa0MfH\nB1euXGEZL58+fQqNRoPXX38dgiDAxsYG77//PtswJQgC5s6dK3kbGPP06VP4+/vD398fCoUCQ4cO\nbbNryPr169l9jI2NNUmq4+zsDGdnZ3zxxRdNPqy3bdvGIrC01mXz7t27uHv3rkl0K41Gg6ysLKac\nS72B8/Hjx1Cr1ejTpw/27t2L1NRUpKam4vLly0xhmjlzJgYMGMDu8bhx4yTZTJmZmWkSfUgsLi4u\nJqGGpeL27dus3xonjapbPD09sXz5chw/flySPUWnT59m596+fTt77paVlWHgwIFMUff29sbRo0dZ\nhsrKykrMmDGD/dbe3r7NdWkpFRUVmDt3Lgst2hpKS0uRkJDA4v+LxqnZs2dj9uzZuHbtGnQ6Ha5c\nuYL8/Pw27SkqKyvDunXrYG1t3aR/fK9evTBp0iSWa2T16tWtltkaMjIy2DPMxsbmpcfLpozv3r2b\nVcQSqXybi1yWcTH74bFjx9C/f/8GO0vfvn1brKTq9XqMGDECSqUSCxYsaPbv6m76HDFiBEaMGNHS\ny6qHwWDATz/9hMmTJ7OHmpioZO/evWZ7E62pqUFsbKzJpCZnRIC6bNy4EYJQG2FCq9VKLlu0WG3Y\nsIFdo7gBrbq6mimrcimnLaWyspKlV2/Nb40zpalUKkyfPh1nzpxh0VXE0qlTpyazz7WU6upqfPDB\nByziRu/evdG7d2/ZQpNptVp4eHjA1dWVpbZfunQpFIraxDerVq1qMG77kiVLoFAomM9ocxENJNOm\nTcO0adMaVXLFjK7GWW49PDzg4eEh6WpTXXJzc5kSLvqOS6EUrl27tp5CqFQq8d5777F9AS9j165d\n7Hfiy0tL0Ol0GDhwoEl0K9GgAgBvv/02U2Skjnpx/fp1tvm77spex44dMXv2bGg0Gty4cQM3btyQ\nRGZmZiZcXV3rtfnkyZMlOX9DVFVV4cqVK1i9ejUOHjzIyqNHj/Do0SN0794drq6u9fy524qxMi4G\nkMjKykLPnj2Z4WDp0qUN9uXs7GyLKuNAre95a4wn+fn5WL9+fYMrd00VR0dHzJs3Dzt37mx1Xoqq\nqiqcPn0ab7/9NkaNGmVSjI0HgtC6VXJxY3dr5zu9Xo8JEyZYVhmvrq5m6cq3b99ulnS6rUVMFBQY\nGChJBIjz588jPDwcnTp1MlEoGipKpbJVG++M3VQ2bdrUrKVquZTxhsjIyIAgCC9NyiMl+fn5rE2V\nSqUkmcVagr+/Pzp27CibG1ZUVBSioqJY35k2bZqJrNWrV0MQakNp/RJ4+vRpqy3j5eXlcHFxwZw5\nc3DhwgWTOefu3bvsvHPmzJF0NUur1WLChAmsDw0dOpRFsZGTMWPGwMnJCWFhYQgLC4Mg1CYta6wP\nf/PNN/Dw8GhVsiEbGxuoVKpmKZOPHz9mL3nGpV+/frLEs09LS4O3t7fJBk6pxvGRI0fqKYUt3Yi7\naNEiKJVK+Pn5oby8vMV9LzIykinhSmVtTG1jRWTz5s1sfj5w4ECLzt0campqUFlZieTkZJbt9MKF\nC7KsWjakiPv5+eHu3bsWcVUVw1MKgoAjR45Ifv7Hjx8zS624aTI5OZmNmabmkKKiIosr42LGyJCQ\nkGYdn5SUBD8/P6hUKgiCACsuODzpAAAX+ElEQVQrK/Tt2xcRERGIiIjAtm3b8PHHH7PVPuPy7bff\nIiIiAh4eHrC1tYVSqZTEPcaY0tJSlp1TEIRW5WuYM2cO0w0jIyNb/PJdU1PDxryVlRWuXr3aZDQX\nWZTx+/fvQ6FQYPTo0S2qvBTk5uZi586dzbZ0jx49GkqlEkFBQW1SxnU6HebNm9fosphYxAQu4eHh\nrU7scPv2bRPFWgwrFxcX12gK9sWLF5v85sMPP8SHH37Y6uttii1btsDR0dFslmmtVos5c+ZAEGpT\nsQcGBppFrkheXh6sra3xzjvvyHL+y5cvm7gYeXp61uurvyRlvLy8nC15d+nSRbLzVlZWYv78+Wws\nSamwFBQUYOjQoWyM7N69W1YLsDHGLj2CUOvbWNcSX1FRgWvXrrHJ3d/fv1Xxi4nIxBrbVJ3EePI9\nevRAfHw8c30ThFo/28mTJ0uW2TY3N5f5z/r7+zc7PG1zqaioQJ8+fUyUw5iYmJcq1AaDAQUFBVi0\naBGsrKxgbW2Nzz77rFV1WLVqlYkyXjfle0VFBTp27AiFQoGYmJhWyWgOYubCl7nltJaqqioT1xS1\nWo2PPvqoRe5UUlJaWsryagwdOlS2VS5xFUlcTRsxYgT7uyH3Nr1ej7S0NISEhLRKGddqtY0mM2ou\n2dnZmDlzJlQqFaysrBrVH+ri7OyMrl27Yu3atTh27Firs8UWFxez3DNSM2vWLAiCAGdn51b9/vHj\nx/D392ehC3v27IklS5YgJyeHhTZsiry8PJOwqC9bxZRUGRcr6Ovri/bt25stna4xEydOZGGTzp07\nZzKhi/XPzs7G8uXL2QYxMVxZW9BqtVixYgXs7OwwYMAA7Ny5Ezt37mRKyahRo5CZmQmdTofMzMw2\nhUZrLEZt3fBgCxYsQGxsLJYtW2byvdzJRGJjY1s9AFqDaIFo3769bMlnmkJ8A5ervxtn4OzQoYNJ\nKD+dTocDBw6wjXhSb/5qKcnJyVCr1cy95GVhp1qCaDEXH3ZSpTR+/vw5unTpAqWyNgHL559/bjb3\nKsB0mVosYgiyZcuWoX///mzzl6+vb5vCGg4cOBBqtbpBy65er8f+/fsxatQoFqN/+vTp7KEjzp+7\ndu1iCoRKpWpzMjPRR1z0D79//36bztcY6enp9azjarWapYC/efMmnjx5gqVLl7LPIiMj2bEdO3Zs\n9T6U/Px8k3jpK1eurPcMuHHjBlQqFTp16iRbdsSKigq4uroiLCxMNmPJ/fv3Tdr4o48+kkVOczAY\nDIiOjmYJhp48eSKbrFGjRjVogLO1tcWWLVuYxTw+Ph7dunVD586d2TFioqBDhw41W563tzc7Z0sQ\nU7dv3ryZZVFWKBTQaDTNPoder5csvKvBYJB8L86ZM2eY1d7Ly6tN58rJyWFJysQi5oRYtWoVVq1a\nhaysLJSXlzM3pKKiIgQGBrLjXV1dXypHUmX8/v37zCo+Z86cNjVAa0lKSmKWB2N/8EGDBjGfR+Pv\nFAoFevfuLdvmzVOnTsHR0RH9+/eXfPJLTk7G8uXLWxxNpblvv61l9+7dZlPGtVotizdtKUV01qxZ\nUCgUsllSjZXxwMBANgHv2bMHvr6+7DsxK6g5KS4uxieffIKpU6di6tSpTBEPCgqqly2zrXz22WcQ\nBAGbNm3Cpk2bJDvv9OnTmWLW0gebFISHh8PBwQGffPIJPvnkEyxYsADDhw+Hq6srbGxsMHz4cLzz\nzjtISUlp80MrPT2d+Ww6OjoiJiYGMTExCAgIYHs9xO937drV6EuJ6PKQkZHRpk14xj7iISEhkmVg\nbIicnByWgbKp0lCEqtDQ0DYFIdDr9SaRM+pGsqqoqGBGk8GDB7f1UhslKioK9vb2srmO5ufnm0RN\n0Wg0Zssx0RBiduj4+HjJXSHqotPpoNFo4OvrC19f30ajbhmXoKAgrF69ulUbgokIrq6ucHV1xWef\nfcbiqt+5c4dt0hVLU0mB3n777WYlz/k1IWamdnNzkyQPRU5ODnx8fGBnZ1fP+GlcPDw8sHbtWhNF\n3NbWtlmrfJIq46Lrg5hUxlLs3LmTPViMLcVi5xP/7+HhIUmowabIyMjAW2+9hZkzZ8oyKVVVVeGH\nH37ADz/8wB7qL1PG5bYcP3z4EI6OjrLvFdBqtcwa0b9/f0kTsTQHvV4PvV6Pvn37YsiQIbLJEUMq\nNlY8PT0RFxfX5pcBjUYDjUbDlH0xlGDdkpSUBI1GgylTptRLEjRgwAAcPHhQlntvb28PT0/PNi/N\nGlNaWgoPDw8EBARg3759kpyzpezZswcDBgyo97lWq5XcSGAwGLBjx44G+5GdnR0iIyNx7do1s7iY\nvXjxgvmItyWhT0vo168flEolDhw4gJSUFJw+fRr9+vVjnxvPl9HR0YiPj0dhYWGb26O4uBju7u7s\nAT1ixAhcuXIFmZmZSE1NhZ+fH5RKJXx8fCS60vo8ePAANjY2smX5BEx98xtytzI3ISEhCA4OZgEV\nzEFBQQEKCgrQuXNnqNVqZj0Vy8cff8xcFtrycn3o0CGoVCqoVCrWrzp27AgrK6t6q+RiCQ4Oxtix\nYzF27FgcOnQIZWVlZjfgyI1xIIfY2FhJz/3s2TMsXLiQrWSIRZw36haVSoV58+Y169ySKeOVlZVw\ncXFh/jWiMl5cXIwTJ07gxIkTZr3peXl52LdvH1teUCqVzDK+cuVKbNmyRbb4xHWZM2cONBqNWTau\nJCcnY926dejVqxebFMeOHYtevXohIiLCbKnDXV1dZd/4tmfPHghCbTSA1vjPthXRr1WMaiEXWq2W\nZbg0Vp6USiUSEhJaHGe6MV5mxWmo2NvbY8SIEWxzilwrTHv37oVKpcLNmzclPa8Yf/rw4cOSnrcl\nZGRkoEuXLpK+ZDSFwWBg4UbrFnORkZHBLMU9e/aUPLrFL5EZM2aY+IzXVf79/f1x5swZ2eR/9dVX\nCA4OlrWPPX36FL6+vrJsymsplZWV8PLywsGDBy1aDzkR8wUEBQXBxcUFNjY28PLygpeXF0aMGIEN\nGzYgLi4OCQkJsj6jfgmIEceMI9iYK4jEuXPn8N5775ko4k5OTi3a99KYvs0zcHI4HA6Hw+FwOBZC\nKCoqQmNftm/fvt5nAOgvf/kLERHFxcWRk5MT2draklarpYkTJ9Kf/vQnGjJkCCkUXM//LTB//nya\nOXMmBQYGynL+/Px86tGjB5WUlFBERAStW7dOFjlN8f333xMR0aBBg+jFixcNjotfEwkJCfTgwQP2\n99///nfKzc2td9ybb75Jnp6eNHnyZPLz8yNnZ2fZ6zZjxgw6evQoZWdnk62trezyOPLx7NkzCgoK\nomfPntGQIUPo0KFDZGNjY+lqyU5ZWRmdO3eOiIhCQ0PZ5wBoypQpFBsbS+7u7haqHefXTllZGRUW\nFpKXl5elq2IRrl27RkREffv2JSKijRs30sKFCy1ZpRZRXFzc4OctVsaJiLRaLRERDRkyhLKzsykm\nJoa6du1Kb7zxBlfCf2OkpKTQsWPHaNmyZZKfGwBt376dwsPDaeHChbRx40bJZXA4HOmprq6m3r17\nU2pqKg0cOJCSkpJIrVZbulocDudXTllZGRERdenShYYOHUrbtm0jlUpl4Vo1H0mVcQ7HHOTl5ZG7\nuzt5e3vT9evXydXV1dJV4nA4zeD27dvUs2dPsrW1pfv373NLMIfD4RBXxjkcDofD4XA4HIvRmDJu\n1ZofcTgcDofD4XA4nLbDHbw5HA6Hw+FwOBwLwZVxDofD4XA4HA7HQnBlnMPhcDgcDofDsRBcGedw\nOBwOh8PhcCwEV8Y5HA6Hw+FwOBwLwZVxDofD4XA4HA7HQnBlnMPhcDgcDofDsRCSK+OKe/fIdtw4\ncvD1JQc/P7IdO5YU6elSi2mQ9o6O1O53v6N2HTqwYjd0qOxyhZwcspk+nRw6dyYHX1+yHT+eFA8e\nyC/3yROynTiRHH7/e3L4wx/I5t13iUpLZZerSE0l2zFjau+xry+pY2KIKitll3vjxg36t3/7t3ql\nT58+dOPGDdnli3z55ZfUp08f+vHHH2WXlZWVRdHR0TR48GAaMmQIRUREUGZmpuxy09PTae7cuTRo\n0CD6j//4D4qOjqasrCzZ5RpjznYmInr48CFNnjyZ/v3f/90s8oxJTEykUaNGUf/+/Wnq1KmUkpIi\nu8zc3Fxavnw5DRs2jAYPHkzR0dH06NEj2eXeu3ePFixYQIMHD6bBgwfThg0bSK/Xyy5XxBJtbalx\nbMxvaTwRmf96iSzTtyw1V1tq/jDG3PdYzraWVhkHyHbcODJ4elJpWhqV3r5NBi8vshs3jgiNJvqU\nlPKvv6aS3FxWyr/7TnaZdhMmEBFR6bVrVJqcTKRSke20abLLtZ06lWBrS2XXrlHZ+fOkePyYbBYu\nlFdoURHZjR1LBj8/Kr11i8r+8Q9SpqWRevVqeeUS0WuvvUb/+Mc/TEpsbCx17NiR/vjHP8oun4jo\n6dOn9OWXX5pFFgCKjo4mNzc3+u///m/65ptvyMPDg6KjowkyjqfS0lKaP38+/cu//At99913dPDg\nQVKr1bRkyRLZZNbFnO1MRHTy5EmaP38+eXl5mU2myOHDhykxMZE2bNhAJ0+epMGDB9P27dvJYDDI\nKjcmJoaIiL766iv6+uuvSaVS0fLly2WVWVpaShEREeTl5UWHDx+mvXv30v3790mj0cgqV8QSbW2p\ncWzMb2k8EZn/eoks07csOVdbYv4wxtz3WO62llQZFwoKSJmVRVXjxhHZ2hLZ2lLV+PGkePyYhMJC\nKUX9ciguppoePUj34YdEjo5Ejo5UOXs2KW/fJioqkk2sIiWFrK5dI92aNQQnJ4K7O+lWrCDrr78m\n4cUL2eRaXb1KwvPntdfbrh2hY0fSrVlDqi++IKqqkk1uQ2i1WoqLi6OYmBh65ZVXzCJz/fr1NG7c\nOLPIKioqopycHBo2bBip1WpSq9U0fPhwevbsmazZcSsrKykyMpKmTZtGKpWKHBwcaPjw4ZSVlUWV\nZlgBITJvOxMRVVRU0K5duyg4ONhsMkU+//xzmjFjBnXp0oXUajW98847pNFoSKGQz4uwrKyMAgIC\nKCIigtq1a0ft2rWjcePG0f3796mkpEQ2uSkpKVRUVEQRERFkb29PHTp0oMjISDp69ChVV1fLJlfE\nEm1tqXFszG9pPBGZ/3qJLNO3LDVXW2r+MMbc91jutpa0l8DVlar79CHV55/XKqIVFaRKTKTqfv0I\nzs5SimqUV/7f/yP7wEBq9+qrZDt+PAnZ2fIKbN+etBoNwcgCoMjOJrRrR+TgIJtYZXIyGdzcCB4e\n7LOaXr1IqKkh5a1bsskl4H+K+JGTEwklJaT46Sf55DbA3/72N/Lx8THbhP/dd99RXl4eTZw40Szy\nnJycqEePHnTkyBEqLS0lnU5HSUlJ1LNnT3J0dJRNrqurK40ePZo9RJ48eUL79++nkJAQs7z0mLud\niYhGjx5Nnp6eZpMnkpeXR48fPyYANHnyZAoJCaHw8HDZl5nt7e1p1apV5O7uzj57+vQp2dnZkZ2d\nnWxyAbAi0q5dOyovL6fHjx/LJpfIcm1tqXEs8lsaT0SWuV5L9S1LzdWWmj9ELHGP5W5ryV/ZKvbs\nIWVyMrX38aH2np6kvHKFKrZvl1pMg1T37k3VffpQ2YULVHr1KlFNTa2LjBksLiLCzz+T+oMPSBcT\nQ6RUyiZH8fw5oe5EbmtLeOUVEgoKZJNb3bcvwdmZ1O+/T1RaSkJeHr2yfj1BoZDVIl+X0tJS2rdv\nH82aNcss8kpKSig+Pp5WrFhBVlZWZpFJRLRu3TrKyMigN954gwYMGEC3bt2i1WZwCSKqnVxff/11\nCg0NJTs7O3rvvfdkl2mpdrYUeXl5RER07NgxWrduHX399dfk6OhICxcupCozrjQ9e/aMEhISaMaM\nGaSUcd7q2bMntW/fnrZu3Url5eVUUFBAu3btIoVCIbuV2JJtbalx/FsbT5a6XkuPY0vM1caYa/4g\nsnyflqutpVXGq6rIbtw4qu7fn0oePqSShw+peuBAshszhkirlVRUQ5SfOkX6yEgie3uCpydpN20i\n5Z07pLx+XXbZRESKtDSyHzaMqv78Z9JHRMgrTBAa9sOX2wfR0ZEq/v53Ut6+Te3++EeyGz2aqkaP\nrq2PtbW8so34+uuvyd/fn3r06GEWefHx8RQSEmI233QiourqaoqOjqagoCA6ceIEnThxgvr06UML\nFiwgnU4nu3wPDw+6dOkSHT58mIiIwsPDZXclsEQ7WxLRQjxp0iR69dVXydHRkaKioujx48eUlpZm\nljo8ePCAwsLCaODAgfTOO+/IKsvBwYE2b95M9+/fp5EjR9K8efPojTfeIEEQZH+wWqqtLTmOf2vj\nyVLXa+lxbIm5WsSc8weR5fu0XG0tqTJu9f33pEhLq/VjdnYmODuTbu1aUmRlkdWFC1KKahbw9iYo\nlSTk5souS/n992T/pz+RfuZM0m3eLLs8g6trfUt0aSkJej0Z3NxklV3TuzeVf/stlWRnU9nly1TT\nowcJNTVkMOOy5MmTJ2ngwIFmkfXjjz/StWvXaO7cuWaRJ3Lt2jV68OABRUREkKOjIzk6OlJkZCQ9\nefLErBECPD09afny5ZSenk63ZHSBslQ7WxIXFxciqnXVEHFzcyOlUkn5+fmyy79+/Tq9++679Oab\nb9KyZctkl0dE1L17d9q5cyedPXuW9u3bRwEBAVRTU0NuMs9blmprS43j39p4suT1Wnoci5hrrhYx\n9/zxS+rTUre15Jbxuv7EVFVFJHNUACIixc2bpF6yxES24sGDWiXR11dW2crkZLKbPJm0mzZRpdzR\nTP5JTVAQKQoKTHzilT/+SHjlFarp1Us+wZWVZL1vn8mLgPWpU1Tj62vivy4nT548oXv37pnNVzwp\nKYkKCwspNDSUhgwZQkOGDCGi2t3kcXFxssmtrq6uF22hurpa9igbp06dovHjx5vIFkPPyWm9tFQ7\nWxI3Nzeyt7ene/fusc9yc3OppqaGPGQeT+np6bRkyRJasmQJTTND9Cei2n507NgxKjLa3H7p0iV6\n9dVX6Xe/+52ssi3V1pYax7+18WTJ67VU37LUXE1kmfnDkvdY7raW9G5V9+tHcHEh9erVpFuxgkih\nIPVHH9Vu7OzbV0pR9UCHDqTat4/Qrh1VLlpEQlER2cTEUPW//isZ/s//kU9wTQ3ZzJtHupgYqnrz\nTfnk1MHwxz9S9b/+K9msWkXa//t/ibRaUsfGkn7CBCKjt3PJUanolQ0bSHnlCunWryfF/fv0yvr1\ntffbTGRkZJBKpSJvb2+zyIuKiqJ3333X5LM///nPtGLFCurTp49scsUNXhqNht59911SKBS0fft2\ncnJyop49e8oqNz8/nxISEigsLIyqq6spISGB3N3d6Q9/+INsci3VzpbEysqKxo4dS59//jm99tpr\n5OnpSVu2bKHf//731K1bN9nk1tTU0Jo1a2j69Ok01Ay5GESsra3p008/pVu3btGiRYvo0aNHtGvX\nLpozZ47ssi3V1pYax7+18WTJ67Vk37LEXG2p+cOS91juthaKiookdTJWpKSQ+v33ayN6AFTTsyfp\nPvxQXoX4nyivXCH16tWk/KePVtWwYaSLjSX8cwlJFpmXL5P98OEElarWb9qI8q+/phoZrbdCXh7Z\nLFxIVufOESmVVBUaStp164hsbGSTSVSb9Mdm4UJSpqURnJyocs4c0i9YIKtMY/7+97/Tnj176Nix\nY2aTWZc+ffrQtm3bKCgoSFY59+7do61bt9KdO3cIAHXp0oUiIiIoICBAVrnp6ekUHx9P6enppFar\nqXv37rRgwQLy8/OTVW5dzNXOb775Jj179oxqamqopqaGVCoVEREtX76c/vSnP8kqW5zUjx8/ThUV\nFRQUFER/+ctfqEOHDrLJvHnzJs2ePZusra1JqDNvbdmyhV577TXZZN+7d4/WrVtHDx48oHbt2tGE\nCRNo0qRJsskzxhJtTWS5cVyX38J4MsZc10tkub5libnakvNHXcx5j+Vsa8mVcQ6Hw+FwOBwOh9M8\n5ItGz+FwOBwOh8PhcJqEK+McDofD4XA4HI6F4Mo4h8PhcDgcDodjIbgyzuFwOBwOh8PhWAiujHM4\nHA6Hw+FwOBaCK+McDofD4XA4HI6F4Mo4h8PhcDgcDodjIbgyzuFwOBwOh8PhWAiujHM4HA6Hw+Fw\nOBbi/wNMSPjhtleHhAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7f60c528f898\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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Fp0+fHrTh39nZqfJhtXPnTty5cyfq6+uzXjqGvLw8FIlE6OnpyXk4Sl/wsWgzJSWF9UKL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\nSUVORK5CYII=\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7f60c5269ac8\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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IyNB4x9evX2c///jxIxuWJYQYT0pK0ghJ2bRpE2ZmZmJmZiavttRJSUnheIi3bNkimC11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\nAoFAIBBE4v8A9i+4oH6kqekAAAAASUVORK5CYII=\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7f60c51d4ac8\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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BLi4uuHr1KrZs2aIsjBo/frwy62EymRRDd+bMGZw5c+ah9eSsKLNnz36g/OiV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\nIBAINEKYcYFAIBAIBAKBQCP+D42qVSYgdOanAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7f60c51bbdd8\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# recognize digits from local fonts\n",
        "probabilities = trained_model.predict(font_digits, steps=1)\n",
        "predicted_labels = np.argmax(probabilities, axis=1)\n",
        "display_digits(font_digits, predicted_labels, font_labels, \"predictions from local fonts (bad predictions in red)\", N)\n",
        "\n",
        "# recognize validation digits\n",
        "probabilities = trained_model.predict(validation_digits, steps=1)\n",
        "predicted_labels = np.argmax(probabilities, axis=1)\n",
        "display_top_unrecognized(validation_digits, predicted_labels, validation_labels, N, 7)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "5tzVi39ShrEL"
      },
      "source": [
        "## Deploy the trained model to ML Engine\n",
        "\n",
        "Push your trained model to production on ML Engine for a serverless, autoscaled, REST API experience.\n",
        "\n",
        "You will need a GCS bucket and a GCP project for this.\n",
        "Models deployed on ML Engine autoscale to zero if not used. There will be no ML Engine charges after you are done testing.\n",
        "Google Cloud Storage incurs charges. Empty the bucket after deployment if you want to avoid these. Once the model is deployed, the bucket is not useful anymore."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "3Y3ztMY_toCP"
      },
      "source": [
        "### Configuration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "cellView": "both",
        "colab": {},
        "colab_type": "code",
        "id": "iAZAn7yIhqAS"
      },
      "outputs": [],
      "source": [
        "PROJECT = \"\" #@param {type:\"string\"}\n",
        "BUCKET = \"gs://\"  #@param {type:\"string\", default:\"jddj\"}\n",
        "NEW_MODEL = True #@param {type:\"boolean\"}\n",
        "MODEL_NAME = \"colabmnist\" #@param {type:\"string\"}\n",
        "MODEL_VERSION = \"v0\" #@param {type:\"string\"}\n",
        "\n",
        "assert PROJECT, 'For this part, you need a GCP project. Head to http://console.cloud.google.com/ and create one.'\n",
        "assert re.search(r'gs://.+', BUCKET), 'For this part, you need a GCS bucket. Head to http://console.cloud.google.com/storage and create one.'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "GxQTtjmdIbmN"
      },
      "source": [
        "### Export the model for serving from ML Engine"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "GOgh7Kb7SzzG"
      },
      "outputs": [],
      "source": [
        "class ServingInput(tf.keras.layers.Layer):\n",
        "  # the important detail in this boilerplate code is \"trainable=False\"\n",
        "  def __init__(self, name, dtype, batch_input_shape=None):\n",
        "    super(ServingInput, self).__init__(trainable=False, name=name, dtype=dtype, batch_input_shape=batch_input_shape)\n",
        "  def get_config(self):\n",
        "    return {'batch_input_shape': self._batch_input_shape, 'dtype': self.dtype, 'name': self.name }\n",
        "\n",
        "  def call(self, inputs):\n",
        "    # When the deployed model is called through its REST API,\n",
        "    # the JSON payload is parsed automatically, transformed into\n",
        "    # a tensor and passed to this input layer. You can perform\n",
        "    # additional transformations, such as decoding JPEGs for example,\n",
        "    # before sending the data to your model. However, you can only\n",
        "    # use tf.xxxx operations.\n",
        "    return inputs\n",
        "\n",
        "# little wrinkle: must copy the model from TPU to CPU manually. This is a temporary workaround.\n",
        "tf_logging.set_verbosity(tf_logging.INFO)\n",
        "restored_model = model\n",
        "restored_model.set_weights(trained_model.get_weights()) # this copied the weights from TPU, does nothing on GPU\n",
        "tf_logging.set_verbosity(tf_logging.WARN)\n",
        "\n",
        "# add the serving input layer\n",
        "serving_model = tf.keras.Sequential()\n",
        "serving_model.add(ServingInput('serving', tf.float32, (None, 28*28)))\n",
        "serving_model.add(restored_model)\n",
        "export_path = tf.contrib.saved_model.save_keras_model(serving_model, os.path.join(BUCKET, 'keras_export'))  # export he model to your bucket\n",
        "export_path = export_path.decode('utf-8')\n",
        "print(\"Model exported to: \", export_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "zy3T3zk0u2J0"
      },
      "source": [
        "### Deploy the model\n",
        "This uses the command-line interface. You can do the same thing through the ML Engine UI at https://console.cloud.google.com/mlengine/models\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "nGv3ITiGLPL3"
      },
      "outputs": [],
      "source": [
        "# Create the model\n",
        "if NEW_MODEL:\n",
        "  !gcloud ml-engine models create {MODEL_NAME} --project={PROJECT} --regions=us-central1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "o3QtUowtOAL-"
      },
      "outputs": [],
      "source": [
        "# Create a version of this model (you can add --async at the end of the line to make this call non blocking)\n",
        "# Additional config flags are available: https://cloud.google.com/ml-engine/reference/rest/v1/projects.models.versions\n",
        "# You can also deploy a model that is stored locally by providing a --staging-bucket=... parameter\n",
        "!echo \"Deployment takes a couple of minutes. You can watch your deployment here: https://console.cloud.google.com/mlengine/models/{MODEL_NAME}\"\n",
        "!gcloud ml-engine versions create {MODEL_VERSION} --model={MODEL_NAME} --origin={export_path} --project={PROJECT} --runtime-version=1.10"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "jE-k1Zn6kU2Z"
      },
      "source": [
        "### Test the deployed model\n",
        "Your model is now available as a REST API. Let us try to call it. The cells below use the \"gcloud ml-engine\"\n",
        "command line tool but any tool that can send a JSON payload to a REST endpoint will work."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "zZCt0Ke2QDer"
      },
      "outputs": [],
      "source": [
        "# prepare digits to send to online prediction endpoint\n",
        "digits = np.concatenate((font_digits, validation_digits[:100-N]))\n",
        "labels = np.concatenate((font_labels, validation_labels[:100-N]))\n",
        "with open(\"digits.json\", \"w\") as f:\n",
        "  for digit in digits:\n",
        "    # the format for ML Engine online predictions is: one JSON object per line\n",
        "    data = json.dumps({\"serving_input\": digit.tolist()})  # \"serving_input\" because the ServingInput layer was named \"serving\". Keras appends \"_input\"\n",
        "    print(data, file=f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "colab_type": "code",
        "id": "n6PqhQ8RQ8bp",
        "outputId": "434953b5-c1b0-4964-8dcf-2119361839e9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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JYpg2fAPOA/qj0V8TkmuiR2uDx8rPEPefzxER3B0kLym9hv+ugFXzZoK8Rxs3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\nw0D8P/Cxxt5pnjHTAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7f60c32a5c18\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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PnpLW+9lnn+n8nps3b27280+fPhUFszBrL0XEwPvvvy+Ke1NT0waa5/333xc/M2jQoHbb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\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7f60c51d4ba8\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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47t27KTg4mIKDg8nV1ZU8PT2pf//+NGfOHDpx4oRsC2UIy2Y3N6R9p709zdGU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\nxkn8P6zE45GFuENTAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7f60c5317908\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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o0Fe6uLjIqnOFh4eDiDBs2LAWW/fFlUilXcE2bdokPfugoCBZzx0aGgoLCwtYWFg0a3wq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\nYDAYDAaDoRL/BzPuPTnl2Nq3AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7f60c5212630\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Request online predictions from deployed model (REST API) using the \"gcloud ml-engine\" command line.\n",
        "predictions = !gcloud ml-engine predict --model={MODEL_NAME} --json-instances digits.json --project={PROJECT} --version {MODEL_VERSION}\n",
        "print(predictions)\n",
        "\n",
        "probabilities = np.stack([json.loads(p) for p in predictions[1:]]) # first line is the name of the input layer: drop it, parse the rest\n",
        "predictions = np.argmax(probabilities, axis=1)\n",
        "display_top_unrecognized(digits, predictions, labels, N, 100//N)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "2a5cGsSTEBQD"
      },
      "source": [
        "## What's next\n",
        "\n",
        "* Learn about [Cloud TPUs](https://cloud.google.com/tpu/docs) that Google designed and optimized specifically to speed up and scale up ML workloads for training and inference and to enable ML engineers and researchers to iterate more quickly.\n",
        "* Explore the range of [Cloud TPU tutorials and Colabs](https://cloud.google.com/tpu/docs/tutorials) to find other examples that can be used when implementing your ML project.\n",
        "\n",
        "On Google Cloud Platform, in addition to GPUs and TPUs available on pre-configured [deep learning VMs](https://cloud.google.com/deep-learning-vm/),  you will find [AutoML](https://cloud.google.com/automl/)*(beta)* for training custom models without writing code and [Cloud ML Engine](https://cloud.google.com/ml-engine/docs/) which will allows you to run parallel trainings and hyperparameter tuning of your custom models on powerful distributed hardware.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "SVY1pBg5ydH-"
      },
      "source": [
        "## License"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "hleIN5-pcr0N"
      },
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "author: Martin Gorner\u003cbr\u003e\n",
        "twitter: @martin_gorner\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "Copyright 2018 Google LLC\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "you may not use this file except in compliance with the License.\n",
        "You may obtain a copy of the License at\n",
        "\n",
        "    http://www.apache.org/licenses/LICENSE-2.0\n",
        "\n",
        "Unless required by applicable law or agreed to in writing, software\n",
        "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "See the License for the specific language governing permissions and\n",
        "limitations under the License.\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "This is not an official Google product but sample code provided for an educational purpose\n"
      ]
    }
  ],
  "metadata": {
    "accelerator": "TPU",
    "colab": {
      "collapsed_sections": [
        "N6ZDpd9XzFeN"
      ],
      "name": "MNIST TPU.ipynb",
      "provenance": [],
      "toc_visible": true,
      "version": "0.3.2"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
